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Jose Olmo

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Calvo Pardo, Héctor & Olmo, Jose & Mancini, Tullio, 2021. "Machine Learning the Carbon Footprint of Bitcoin Mining," CEPR Discussion Papers 16267, C.E.P.R. Discussion Papers.

    Cited by:

    1. Yerushalmi, Erez & Paladini, Stefania, 2023. "Blockchain in Financial Intermediation and Beyond: What are the Main Barriers for Widespread Adoption?," CAFE Working Papers 22, Centre for Accountancy, Finance and Economics (CAFE), Birmingham City Business School, Birmingham City University.
    2. Nishant Sapra & Imlak Shaikh & Ashutosh Dash, 2023. "Impact of Proof of Work (PoW)-Based Blockchain Applications on the Environment: A Systematic Review and Research Agenda," JRFM, MDPI, vol. 16(4), pages 1-29, March.

  2. Luciano De Castro & Antonio F. Galvao & Gabriel Montes Rojas & José Olmo, 2020. "Portfolio Selection in Quantile Decision Models," Working Papers 11, Red Nacional de Investigadores en Economía (RedNIE).

    Cited by:

    1. Balter, Anne G. & Chau, Ki Wai & Schweizer, Nikolaus, 2024. "Comparative risk aversion vs. threshold choice in the Omega ratio," Omega, Elsevier, vol. 123(C).

  3. Gonzalo, Jesús & Olmo, José, 2016. "Long-term optimal portfolio allocation under dynamic horizon-specific risk aversion," UC3M Working papers. Economics 23599, Universidad Carlos III de Madrid. Departamento de Economía.

    Cited by:

    1. Jozef Barunik & Josef Kurka, 2021. "Risks of heterogeneously persistent higher moments," Papers 2104.04264, arXiv.org, revised Mar 2024.
    2. Jamel Boukhatem, 2021. "Sukuk Market and Economic Welfare Nexus: A Partial Equilibrium Approach," International Journal of Economics and Financial Issues, Econjournals, vol. 11(3), pages 142-145.

  4. Jose Olmo & William Pouliot, 2014. "Tests to Disentangle Breaks in Intercept from Slope in Linear Regression Models with Application to Management Performance in the Mutual Fund Industry," Discussion Papers 14-02, Department of Economics, University of Birmingham.

    Cited by:

    1. Pouliot, William, 2016. "Robust tests for change in intercept and slope in linear regression models with application to manager performance in the mutual fund industry," Economic Modelling, Elsevier, vol. 58(C), pages 523-534.

  5. Iori, G. & Kapar, B. & Olmo, J., 2012. "The Cross-Section of Interbank Rates: A Nonparametric Empirical Investigation," Working Papers 12/03, Department of Economics, City University London.

    Cited by:

    1. Markus Engler & Vahidin Jeleskovic, 2016. "Intraday volatility, trading volume and trading intensity in the interbank market e-MID," MAGKS Papers on Economics 201648, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    2. Olivier Brossard & Susanna Saroyan, 2016. "Hoarding and short-squeezing in times of crisis: Evidence from the Euro overnight money market," Post-Print hal-01293693, HAL.
    3. Anastasios Demertzidis & Vahidin Jeleskovic, 2021. "Empirical Estimation of Intraday Yield Curves on the Italian Interbank Credit Market e-MID," JRFM, MDPI, vol. 14(5), pages 1-23, May.
    4. Miguel Sarmiento & Jorge Cely & Carlos León, 2015. "Monitoring the Unsecured Interbank Funds Market," Borradores de Economia 14080, Banco de la Republica.
    5. Vahidin Jeleskovic & Anastasios Demertzidis, 2018. "Comparing different methods for the estimation of interbank intraday yield curves," MAGKS Papers on Economics 201839, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    6. Annika Birch & Tomaso Aste, 2014. "Systemic Losses Due to Counter Party Risk in a Stylized Banking System," Papers 1402.3688, arXiv.org.
    7. Brossard, Olivier & Saroyan, Susanna, 2016. "Hoarding and short-squeezing in times of crisis: Evidence from the Euro overnight money market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 40(C), pages 163-185.

  6. Rafael González-Val & Jose Olmo, 2011. "Growth in a cross-section of cities: location, increasing returns or random growth?," Working Papers 2011/39, Institut d'Economia de Barcelona (IEB).

    Cited by:

    1. Catalina Bolancé & Zuhair Bahraoui & Ramon Alemany, 2015. "Estimating extreme value cumulative distribution functions using bias-corrected kernel approaches," Working Papers XREAP2015-01, Xarxa de Referència en Economia Aplicada (XREAP), revised Jan 2015.
    2. Anna Castañer & Mª Mercè Claramunt, 2014. "Optimal stop-loss reinsurance: a dependence analysis," Working Papers XREAP2014-04, Xarxa de Referència en Economia Aplicada (XREAP), revised Apr 2014.
    3. Esther Vayá & José Ramón García & Joaquim Murillo & Javier Romaní & Jordi Suriñach, 2016. "“Economic Impact of Cruise Activity: The Port of Barcelona”," AQR Working Papers 201609, University of Barcelona, Regional Quantitative Analysis Group, revised Nov 2016.
    4. Mercedes Ayuso & Montserrat Guillén & Jens Perch Nielsen, 2016. "Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data," Working Papers XREAP2016-08, Xarxa de Referència en Economia Aplicada (XREAP), revised Dec 2016.
    5. Anna Castañer & Mª Mercè Claramunt & Alba Tadeo & Javier Varea, 2016. "Modelización de la dependencia del número de siniestros. Aplicación a Solvencia II," Working Papers XREAP2016-01, Xarxa de Referència en Economia Aplicada (XREAP), revised Sep 2016.

  7. Kapar, B. & Olmo, J., 2011. "The determinants of credit default swap spreads in the presence of structural breaks and counterparty risk," Working Papers 11/02, Department of Economics, City University London.

    Cited by:

    1. Syed Jawad Hussain Shahzad & Safwan Mohd Nor & Nur Azura Sanusi & Ronald Ravinesh Kumar, 2018. "The Determinants of Credit Risk: Analysis of US Industry-level Indices," Global Business Review, International Management Institute, vol. 19(5), pages 1152-1165, October.
    2. Shahzad, Syed Jawad Hussain & Nor, Safwan Mohd & Kumar, Ronald Ravinesh & Mensi, Walid, 2017. "Interdependence and contagion among industry-level US credit markets: An application of wavelet and VMD based copula approaches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 310-324.
    3. Miroslav Mateev & Elena Marinova, 2019. "Relation between Credit Default Swap Spreads and Stock Prices: A Non-linear Perspective," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 43(1), pages 1-26, January.
    4. Bratis, Theodoros & Laopodis, Nikiforos T. & Kouretas, Georgios P., 2020. "Systemic risk and financial stability dynamics during the Eurozone debt crisis," Journal of Financial Stability, Elsevier, vol. 47(C).

  8. Gonzalo, Jesús & Olmo, José, 2010. "Conditional stochastic dominance tests in dynamic settings," UC3M Working papers. Economics we1029, Universidad Carlos III de Madrid. Departamento de Economía.

    Cited by:

    1. Linton, Oliver & Seo, Myung Hwan & Whang, Yoon-Jae, 2023. "Testing stochastic dominance with many conditioning variables," Journal of Econometrics, Elsevier, vol. 235(2), pages 507-527.
    2. Arvanitis, Stelios & Scaillet, Olivier & Topaloglou, Nikolas, 2020. "Spanning tests for Markowitz stochastic dominance," Journal of Econometrics, Elsevier, vol. 217(2), pages 291-311.
    3. E. Agliardi & M. Pinar & T. Stengos, 2014. "Assessing temporal trends and industry contributions to air and water pollution using stochastic dominance," Working Papers wp981, Dipartimento Scienze Economiche, Universita' di Bologna.
    4. Agliardi, Elettra & Agliardi, Rossella & Pinar, Mehmet & Stengos, Thanasis & Topaloglou, Nikolas, 2012. "A new country risk index for emerging markets: A stochastic dominance approach," Journal of Empirical Finance, Elsevier, vol. 19(5), pages 741-761.
    5. Olmo, José & Sanso-Navarro, Marcos, 2012. "Forecasting the performance of hedge fund styles," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2351-2365.
    6. Mehmet Pinar & Thanasis Stengos & Nikolas Topaloglou, 2022. "Stochastic dominance spanning and augmenting the human development index with institutional quality," Annals of Operations Research, Springer, vol. 315(1), pages 341-369, August.

  9. González-Val, Rafael & Olmo, Jose, 2010. "A Statistical Test of City Growth: Location, Increasing Returns and Random Growth," MPRA Paper 27139, University Library of Munich, Germany.

    Cited by:

    1. Zhihong Chen & Shihe Fu & Dayong Zhang, 2013. "Searching for the Parallel Growth of Cities," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.

  10. Galvao Jr, A. F. & Montes-Rojas, G. & Olmo, J., 2009. "Threshold quantile autoregressive models," Working Papers 09/05, Department of Economics, City University London.

    Cited by:

    1. Christis Katsouris, 2023. "Structural Break Detection in Quantile Predictive Regression Models with Persistent Covariates," Papers 2302.05193, arXiv.org.
    2. Tang, Yanlin & Song, Xinyuan & Zhu, Zhongyi, 2015. "Threshold effect test in censored quantile regression," Statistics & Probability Letters, Elsevier, vol. 105(C), pages 149-156.
    3. Liu Xiaochun & Luger Richard, 2018. "Markov-switching quantile autoregression: a Gibbs sampling approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 22(2), pages 1, April.
    4. Chung-Ming Kuan & Christos Michalopoulos & Zhijie Xiao, 2017. "Quantile Regression on Quantile Ranges – A Threshold Approach," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(1), pages 99-119, January.
    5. Camille Aït-Youcef, 2019. "How index investment impacts commodities : A story about the financialization of agricultural commodities," Post-Print hal-03484371, HAL.
    6. Cathy Chen & Richard Gerlach, 2013. "Semi-parametric quantile estimation for double threshold autoregressive models with heteroskedasticity," Computational Statistics, Springer, vol. 28(3), pages 1103-1131, June.
    7. Tae-Hwan Kim & Dong Jin Lee & Paul Mizen, 2020. "Impulse Response Analysis in Conditional Quantile Models and an Application to Monetary Policy," Working papers 2020rwp-164, Yonsei University, Yonsei Economics Research Institute.
    8. Galvao, Antonio F. & Montes-Rojas, Gabriel & Olmo, Jose, 2009. "Quantile Threshold Effects in the Dynamics of the Dollar/Pound Exchange Rate," The Journal of Economic Asymmetries, Elsevier, vol. 6(2), pages 69-82.
    9. Junho Lee & Ying Sun & Huixia Judy Wang, 2021. "Spatial cluster detection with threshold quantile regression," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
    10. Christis Katsouris, 2023. "Estimation and Inference in Threshold Predictive Regression Models with Locally Explosive Regressors," Papers 2305.00860, arXiv.org, revised May 2023.
    11. Olivier Damette & Beum-Jo Park, 2015. "Tobin Tax and Volatility: A Threshold Quantile Autoregressive Regression Framework," Review of International Economics, Wiley Blackwell, vol. 23(5), pages 996-1022, November.
    12. Lijuan Huo & Tae-Hwan Kim & Yunmi Kim, 2013. "Testing for Autocorrelation in Quantile Regression Models," Working papers 2013rwp-54, Yonsei University, Yonsei Economics Research Institute.
    13. Yunmi Kim & Lijuan Huo & Tae-Hwan Kim, 2020. "Dealing with Markov-Switching Parameters in Quantile Regression Models," Working papers 2020rwp-166, Yonsei University, Yonsei Economics Research Institute.
    14. Chavas, Jean-Paul & Grainger, Corbett & Hudson, Nicholas, 2016. "How should economists model climate? Tipping points and nonlinear dynamics of carbon dioxide concentrations," Journal of Economic Behavior & Organization, Elsevier, vol. 132(PB), pages 56-65.
    15. Neil Foster-McGregor & Anders Isaksson & Florian Kaulich, 2013. "Importing, Productivity and Absorptive Capacity in Sub-Saharan African Manufacturing Firms," wiiw Working Papers 105, The Vienna Institute for International Economic Studies, wiiw.
    16. Jack Fosten & Daniel Gutknecht & Marc-Oliver Pohle, 2023. "Testing Quantile Forecast Optimality," Papers 2302.02747, arXiv.org, revised Oct 2023.
    17. Montes-Rojas, Gabriel, 2017. "Reduced form vector directional quantiles," Journal of Multivariate Analysis, Elsevier, vol. 158(C), pages 20-30.
    18. Martins, Luis F., 2021. "The US debt–growth nexus along the business cycle," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    19. Jean-Paul Chavas & Salvatore Falco, 2017. "Resilience, Weather and Dynamic Adjustments in Agroecosystems: The Case of Wheat Yield in England," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 67(2), pages 297-320, June.

  11. Pouliot, W. & Olmo, J., 2008. "U-statistic Type Tests for Structural Breaks in Linear Regression Models," Working Papers 08/15, Department of Economics, City University London.

    Cited by:

    1. Olmo, Jose & Pilbeam, Keith & Pouliot, William, 2011. "Detecting the presence of insider trading via structural break tests," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2820-2828, November.
    2. Olmo, J. & Pilbeam, K. & Pouliot, W., 2009. "Detecting the Presence of Informed Price Trading Via Structural Break Tests," Working Papers 09/10, Department of Economics, City University London.

  12. Olmo, J. & Pouliot, W., 2008. "Early Detection Techniques for Market Risk Failure," Working Papers 08/09, Department of Economics, City University London.

    Cited by:

    1. Pouliot, W. & Olmo, J., 2008. "U-statistic Type Tests for Structural Breaks in Linear Regression Models," Working Papers 08/15, Department of Economics, City University London.
    2. Pouliot, William, 2016. "Robust tests for change in intercept and slope in linear regression models with application to manager performance in the mutual fund industry," Economic Modelling, Elsevier, vol. 58(C), pages 523-534.

  13. Martinez, O. & Olmo, J., 2008. "A Nonlinear Threshold Model for the Dependence of Extremes of Stationary Sequences," Working Papers 08/08, Department of Economics, City University London.

    Cited by:

    1. Antonio F. Galvao Jr. & Gabriel Montes‐Rojas & Jose Olmo, 2011. "Threshold quantile autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(3), pages 253-267, May.

  14. Escanciano, J. C. & Olmo, J., 2007. "Estimation risk effects on backtesting for parametric value-at-risk models," Working Papers 07/11, Department of Economics, City University London.

    Cited by:

    1. Gilbert Colletaz & Christophe Hurlin & Sessi Tokpavi, 2008. "Backtesting Value-at-Risk : A GMM Duration-based Test," Post-Print halshs-00363168, HAL.
    2. Bontemps, Christian, 2013. "Moment-Based Tests for Discrete Distributions," IDEI Working Papers 772, Institut d'Économie Industrielle (IDEI), Toulouse, revised Oct 2014.
    3. Evers, Corinna & Rohde, Johannes, 2014. "Model Risk in Backtesting Risk Measures," Hannover Economic Papers (HEP) dp-529, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    4. Bontemps, Christian, 2014. "Simple moment-based tests for value-at-risk models and discrete distribution," TSE Working Papers 14-535, Toulouse School of Economics (TSE).

  15. Juan Carlos Escanciano & Jose Olmo, 2007. "Backtesting Parametric Value-at-Risk with Estimation Risk," CAEPR Working Papers 2007-005, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington, revised Sep 2008.

    Cited by:

    1. Gilbert Colletaz & Christophe Hurlin & Sessi Tokpavi, 2008. "Backtesting Value-at-Risk : A GMM Duration-based Test," Post-Print halshs-00363168, HAL.
    2. Elena-Ivona Dumitrescu & Christophe Hurlin & Vinson Pham, 2012. "Backtesting Value-at-Risk: From Dynamic Quantile to Dynamic Binary Tests," Finance, Presses universitaires de Grenoble, vol. 33(1), pages 79-112.
    3. Radu Tunaru, 2015. "Model Risk in Financial Markets:From Financial Engineering to Risk Management," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 9524, June.
    4. Elena-Ivona DUMITRESCU, 2011. "Backesting Value-at-Risk: From DQ (Dynamic Quantile) to DB (Dynamic Binary) Tests," LEO Working Papers / DR LEO 262, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    5. Jean-Paul Laurent & Hassan Omidi Firouzi, 2022. "Market Risk and Volatility Weighted Historical Simulation After Basel III," Working Papers hal-03679434, HAL.
    6. Thiele, Stephen, 2019. "Detecting underestimates of risk in VaR models," Journal of Banking & Finance, Elsevier, vol. 101(C), pages 12-20.
    7. Guangwei Zhu & Zaichao Du & Juan Carlos Escanciano, 2017. "Automatic portmanteau tests with applications to market risk management," Stata Journal, StataCorp LP, vol. 17(4), pages 901-915, December.
    8. Gery Geenens & Richard Dunn, 2017. "A nonparametric copula approach to conditional Value-at-Risk," Papers 1712.05527, arXiv.org, revised Oct 2019.
    9. Juan Carlos Escanciano & Zaichao Du, 2015. "Backtesting Expected Shortfall: Accounting for Tail Risk," CAEPR Working Papers 2015-001, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    10. Rodrigo Mulero & Alfredo García-Hiernaux, 2021. "Forecasting Spanish unemployment with Google Trends and dimension reduction techniques," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 12(3), pages 329-349, September.
    11. Du, Zaichao & Escanciano, Juan Carlos & Zhu, Guangwei, 2023. "The case for CASE: Estimating heterogeneous systemic effects," Journal of Banking & Finance, Elsevier, vol. 157(C).
    12. Kimera Naradh & Retius Chifurira & Knowledge Chinhamu, 2022. "Analysis of stock exchange risk and currency in South African Financial Markets using stable parameter estimation," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 11(1), pages 120-131, January.
    13. Wang, Zheqi & Crook, Jonathan & Andreeva, Galina, 2020. "Reducing estimation risk using a Bayesian posterior distribution approach: Application to stress testing mortgage loan default," European Journal of Operational Research, Elsevier, vol. 287(2), pages 725-738.
    14. Rice, Gregory & Wirjanto, Tony & Zhao, Yuqian, 2020. "Forecasting value at risk with intra-day return curves," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1023-1038.
    15. Banulescu-Radu, Denisa & Hurlin, Christophe & Leymarie, Jeremy & Scaillet, Olivier, 2020. "Backtesting marginal expected shortfalland related systemic risk measures," Working Papers unige:134136, University of Geneva, Geneva School of Economics and Management.
    16. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
    17. Benjamin R. Auer & Benjamin Mögel, 2016. "How Accurate are Modern Value-at-Risk Estimators Derived from Extreme Value Theory?," CESifo Working Paper Series 6288, CESifo.
    18. Mohamed El Ghourabi & Christian Francq & Fedya Telmoudi, 2016. "Consistent Estimation of the Value at Risk When the Error Distribution of the Volatility Model is Misspecified," Journal of Time Series Analysis, Wiley Blackwell, vol. 37(1), pages 46-76, January.
    19. Christian Gouriéroux & Jean-Michel Zakoian, 2012. "Estimation Adjusted VaR," Working Papers 2012-16, Center for Research in Economics and Statistics.
    20. Francq, Christian & Zakoian, Jean-Michel, 2015. "Joint inference on market and estimation risks in dynamic portfolios," MPRA Paper 68100, University Library of Munich, Germany.
    21. Sylvain Benoît & Christophe Hurlin & Christophe Pérignon, 2015. "Implied Risk Exposures," Post-Print hal-01485613, HAL.
    22. Wied, Dominik & Weiß, Gregor N.F. & Ziggel, Daniel, 2016. "Evaluating Value-at-Risk forecasts: A new set of multivariate backtests," Journal of Banking & Finance, Elsevier, vol. 72(C), pages 121-132.
    23. Geenens, Gery & Dunn, Richard, 2022. "A nonparametric copula approach to conditional Value-at-Risk," Econometrics and Statistics, Elsevier, vol. 21(C), pages 19-37.
    24. Christophe Hurlin & Sébastien Laurent & Rogier Quaedvlieg & Stephan Smeekes, 2017. "Risk Measure Inference," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(4), pages 499-512, October.
    25. Sander Barendse & Erik Kole & Dick van Dijk, 2019. "Backtesting Value-at-Risk and Expected Shortfall in the Presence of Estimation Error," Tinbergen Institute Discussion Papers 19-058/III, Tinbergen Institute.
    26. Francq, Christian & Zakoian, Jean-Michel, 2015. "Looking for efficient qml estimation of conditional value-at-risk at multiple risk levels," MPRA Paper 67195, University Library of Munich, Germany.
    27. Michael B. Gordy & Alexander J. McNeil, 2018. "Spectral Backtests of Forecast Distributions with Application to Risk Management," Finance and Economics Discussion Series 2018-021, Board of Governors of the Federal Reserve System (U.S.).
    28. Fritzsch, Simon & Timphus, Maike & Weiß, Gregor, 2024. "Marginals versus copulas: Which account for more model risk in multivariate risk forecasting?," Journal of Banking & Finance, Elsevier, vol. 158(C).
    29. Zolotko, Mikhail & Okhrin, Ostap, 2014. "Modelling the general dependence between commodity forward curves," Energy Economics, Elsevier, vol. 43(C), pages 284-296.
    30. Benjamin Mögel & Benjamin R. Auer, 2018. "How accurate are modern Value-at-Risk estimators derived from extreme value theory?," Review of Quantitative Finance and Accounting, Springer, vol. 50(4), pages 979-1030, May.
    31. D. Th. Vezeris & C. J. Schinas & Th. S. Kyrgos & V. A. Bizergianidou & I. P. Karkanis, 2020. "Optimization of Backtesting Techniques in Automated High Frequency Trading Systems Using the d-Backtest PS Method," Computational Economics, Springer;Society for Computational Economics, vol. 56(4), pages 975-1054, December.
    32. Lönnbark, Carl, 2013. "On the role of the estimation error in prediction of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 37(3), pages 847-853.
    33. Escanciano, Juan Carlos & Pei, Pei, 2012. "Pitfalls in backtesting Historical Simulation VaR models," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2233-2244.
    34. Emese Lazar & Ning Zhang, 2017. "Model Risk of Expected Shortfall," ICMA Centre Discussion Papers in Finance icma-dp2017-10, Henley Business School, University of Reading.
    35. Argyropoulos, Christos & Panopoulou, Ekaterini, 2019. "Backtesting VaR and ES under the magnifying glass," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 22-37.
    36. Bec, Frédérique & Gollier, Christian, 2014. "Cyclicality and term structure of Value-at-Risk within a threshold autoregression setup," IDEI Working Papers 835, Institut d'Économie Industrielle (IDEI), Toulouse.
    37. Christophe Boucher & Jon Danielsson & Patrick Kouontchou & Bertrand Maillet, 2014. "Risk models-at-risk," Post-Print hal-02312332, HAL.
    38. Corradi, Valentina & Fosten, Jack & Gutknecht, Daniel, 2023. "Out-of-sample tests for conditional quantile coverage an application to Growth-at-Risk," Journal of Econometrics, Elsevier, vol. 236(2).
    39. Taylor, James W., 2020. "Forecast combinations for value at risk and expected shortfall," International Journal of Forecasting, Elsevier, vol. 36(2), pages 428-441.
    40. Zaichao Du & Juan Carlos Escanciano, 2015. "A Nonparametric Distribution-Free Test for Serial Independence of Errors," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 1011-1034, December.
    41. Yannick Hoga & Matei Demetrescu, 2023. "Monitoring Value-at-Risk and Expected Shortfall Forecasts," Management Science, INFORMS, vol. 69(5), pages 2954-2971, May.
    42. Daniel Rösch & Harald Scheule, 2014. "Forecasting Mortgage Securitization Risk Under Systematic Risk and Parameter Uncertainty," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 81(3), pages 563-586, September.
    43. León, Ángel & Ñíguez, Trino-Manuel, 2020. "Modeling asset returns under time-varying semi-nonparametric distributions," Journal of Banking & Finance, Elsevier, vol. 118(C).
    44. Evers, Corinna & Rohde, Johannes, 2014. "Model Risk in Backtesting Risk Measures," Hannover Economic Papers (HEP) dp-529, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    45. Jack Fosten & Daniel Gutknecht & Marc-Oliver Pohle, 2023. "Testing Quantile Forecast Optimality," Papers 2302.02747, arXiv.org, revised Oct 2023.
    46. Timo Dimitriadis & Xiaochun Liu & Julie Schnaitmann, 2020. "Encompassing Tests for Value at Risk and Expected Shortfall Multi-Step Forecasts based on Inference on the Boundary," Papers 2009.07341, arXiv.org.
    47. Mateusz Buczyński & Marcin Chlebus, 2021. "GARCHNet - Value-at-Risk forecasting with novel approach to GARCH models based on neural networks," Working Papers 2021-08, Faculty of Economic Sciences, University of Warsaw.
    48. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    49. León, Ángel & Ñíguez, Trino-Manuel, 2021. "The transformed Gram Charlier distribution: Parametric properties and financial risk applications," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 323-349.
    50. Claußen, Arndt & Rösch, Daniel & Schmelzle, Martin, 2019. "Hedging parameter risk," Journal of Banking & Finance, Elsevier, vol. 100(C), pages 111-121.
    51. Lee, Yongwoong & Rösch, Daniel & Scheule, Harald, 2016. "Accuracy of mortgage portfolio risk forecasts during financial crises," European Journal of Operational Research, Elsevier, vol. 249(2), pages 440-456.

  16. Gonzalo, Jesús & Olmo, José, 2005. "Contagion versus flight to quality in financial markets," UC3M Working papers. Economics we051810, Universidad Carlos III de Madrid. Departamento de Economía.

    Cited by:

    1. Francine Gresnigt & Erik Kole & Philip Hans Franses, 2017. "Specification Testing in Hawkes Models," Journal of Financial Econometrics, Oxford University Press, vol. 15(1), pages 139-171.
    2. Felices, Guillermo & Grisse, Christian & Yang, Jing, 2009. "International financial transmission: emerging and mature markets," Bank of England working papers 373, Bank of England.
    3. Kallenberg, Wilbert C.M., 2008. "Modelling dependence," Insurance: Mathematics and Economics, Elsevier, vol. 42(1), pages 127-146, February.
    4. Jammazi, Rania & Tiwari, Aviral Kr. & Ferrer, Román & Moya, Pablo, 2015. "Time-varying dependence between stock and government bond returns: International evidence with dynamic copulas," The North American Journal of Economics and Finance, Elsevier, vol. 33(C), pages 74-93.
    5. Paulo Horta & Carlos Mendes & Isabel Vieira, 2008. "Contagion effects of the US Subprime Crisis on Developed Countries," CEFAGE-UE Working Papers 2008_08, University of Evora, CEFAGE-UE (Portugal).
    6. Dirk G. Baur & Brian M. Lucey, 2007. "Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold," The Institute for International Integration Studies Discussion Paper Series iiisdp198, IIIS.
    7. Grillini, Stefano & Ozkan, Aydin & Sharma, Abhijit, 2022. "Static and dynamic liquidity spillovers in the Eurozone: The role of financial contagion and the Covid-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 83(C).
    8. Silvapulle, Param & Fenech, Jean Pierre & Thomas, Alice & Brooks, Rob, 2016. "Determinants of sovereign bond yield spreads and contagion in the peripheral EU countries," Economic Modelling, Elsevier, vol. 58(C), pages 83-92.
    9. Craig S. Hakkio & William R. Keeton, 2009. "Financial stress: what is it, how can it be measured, and why does it matter?," Economic Review, Federal Reserve Bank of Kansas City, vol. 94(Q II), pages 5-50.
    10. Guidolin, Massimo & Hansen, Erwin & Pedio, Manuela, 2019. "Cross-asset contagion in the financial crisis: A Bayesian time-varying parameter approach," Journal of Financial Markets, Elsevier, vol. 45(C), pages 83-114.
    11. Selcuk Bayraci & Sercan Demiralay & Hatice Gaye Gencer, 2018. "Stock†Bond Co†Movements And Flight†To†Quality In G7 Countries: A Time†Frequency Analysis," Bulletin of Economic Research, Wiley Blackwell, vol. 70(1), pages 29-49, January.
    12. Soylu, Pınar Kaya & Güloğlu, Bülent, 2019. "Financial contagion and flight to quality between emerging markets and U.S. bond market," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    13. Dirk G. Baur, 2007. "Stock-bond co-movements and cross-country linkages," The Institute for International Integration Studies Discussion Paper Series iiisdp216, IIIS.
    14. Ponrajah, Jeremey & Ning, Cathy, 2023. "Stock–bond dependence and flight to/from quality," International Review of Financial Analysis, Elsevier, vol. 86(C).
    15. Withanage, Yeshan & Jayasinghe, Prabhath, 2017. "Volatility Spillovers between South Asian Stock Markets: Evidence from Sri Lanka, India and Pakistan," MPRA Paper 82782, University Library of Munich, Germany, revised Nov 2017.
    16. Baur, Dirk G. & Lucey, Brian M., 2009. "Flights and contagion--An empirical analysis of stock-bond correlations," Journal of Financial Stability, Elsevier, vol. 5(4), pages 339-352, December.
    17. Chiu-Lan Chang & Paul L. Hsueh, 2013. "An Investigation of the Flight-to-Quality Effect: Evidence from Asia-Pacific Countries," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 49(S4), pages 53-69, September.
    18. Robert B. Durand & Markus Junker & Alex Szimayer, 2010. "The flight‐to‐quality effect: a copula‐based analysis," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 50(2), pages 281-299, June.
    19. Paulo Horta & Sérgio Lagoa & Luís Martins, 2016. "Unveiling investor-induced channels of financial contagion in the 2008 financial crisis using copulas," Quantitative Finance, Taylor & Francis Journals, vol. 16(4), pages 625-637, April.

  17. Olmo, José, 2005. "Testing the existence of clustering in the extreme values," UC3M Working papers. Economics we051809, Universidad Carlos III de Madrid. Departamento de Economía.

    Cited by:

    1. Hautsch, Nikolaus & Herrera, Rodrigo, 2015. "Multivariate dynamic intensity peaks-over-threshold models," CFS Working Paper Series 516, Center for Financial Studies (CFS).

  18. Jose Olmo & Jesus Gonzalo, 2004. "Which Extreme Values are Really Extremes?," Econometric Society 2004 North American Winter Meetings 144, Econometric Society.

    Cited by:

    1. Schluter, Christian & Trede, Mark, 2008. "Identifying multiple outliers in heavy-tailed distributions with an application to market crashes," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 700-713, September.
    2. Koopman, Siem Jan & Shephard, Neil & Creal, Drew, 2009. "Testing the assumptions behind importance sampling," Journal of Econometrics, Elsevier, vol. 149(1), pages 2-11, April.
    3. Ana-Maria Gavril, 2009. "Exchange Rate Risk: Heads or Tails," Advances in Economic and Financial Research - DOFIN Working Paper Series 35, Bucharest University of Economics, Center for Advanced Research in Finance and Banking - CARFIB.
    4. Charles, Amélie & Darné, Olivier, 2014. "Large shocks in the volatility of the Dow Jones Industrial Average index: 1928–2013," Journal of Banking & Finance, Elsevier, vol. 43(C), pages 188-199.
    5. Nikola Radivojevic & Milena Cvjetkovic & Saša Stepanov, 2016. "The new hybrid value at risk approach based on the extreme value theory," Estudios de Economia, University of Chile, Department of Economics, vol. 43(1 Year 20), pages 29-52, June.
    6. Loriano Mancini & Fabio Trojani, 2011. "Robust Value at Risk Prediction," Journal of Financial Econometrics, Oxford University Press, vol. 9(2), pages 281-313, Spring.
    7. Wendy Shinyie & Noriszura Ismail & Abdul Jemain, 2013. "Semi-parametric Estimation for Selecting Optimal Threshold of Extreme Rainfall Events," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2325-2352, May.
    8. Bekiros, Stelios D. & Georgoutsos, Dimitris A., 2008. "The extreme-value dependence of Asia-Pacific equity markets," Journal of Multinational Financial Management, Elsevier, vol. 18(3), pages 197-208, July.
    9. Francine Gresnigt & Erik Kole & Philip Hans Franses, 2017. "Exploiting Spillovers to Forecast Crashes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(8), pages 936-955, December.
    10. Olmo, J., 2009. "Extreme Value Theory Filtering Techniques for Outlier Detection," Working Papers 09/09, Department of Economics, City University London.
    11. Bertrand B. Maillet & Jean-Philippe R. M�decin, 2010. "Extreme Volatilities, Financial Crises and L-moment Estimations of Tail-indexes," Working Papers 2010_10, Department of Economics, University of Venice "Ca' Foscari".
    12. Alfonso Novales & Laura Garcia-Jorcano, 2019. "Backtesting Extreme Value Theory models of expected shortfall," Documentos de Trabajo del ICAE 2019-24, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    13. Tae-Hwy Lee & Yong Bao & Burak Saltoglu, 2006. "Evaluating predictive performance of value-at-risk models in emerging markets: a reality check," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(2), pages 101-128.
    14. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    15. Jalal, Amine & Rockinger, Michael, 2008. "Predicting tail-related risk measures: The consequences of using GARCH filters for non-GARCH data," Journal of Empirical Finance, Elsevier, vol. 15(5), pages 868-877, December.

Articles

  1. Jose Olmo, 2023. "A nonparametric predictive regression model using partitioning estimators based on Taylor expansions," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(3), pages 294-318, May.

    Cited by:

    1. Christis Katsouris, 2024. "Robust Estimation in Network Vector Autoregression with Nonstationary Regressors," Papers 2401.04050, arXiv.org.

  2. Richard J. McGee & Jose Olmo, 2022. "Optimal characteristic portfolios," Quantitative Finance, Taylor & Francis Journals, vol. 22(10), pages 1853-1870, October.

    Cited by:

    1. Auh, Jun Kyung & Cho, Wonho, 2023. "Factor-based portfolio optimization," Economics Letters, Elsevier, vol. 228(C).

  3. Luciano de Castro & Antonio F. Galvao & Gabriel Montes-Rojas & Jose Olmo, 2022. "Portfolio selection in quantile decision models," Annals of Finance, Springer, vol. 18(2), pages 133-181, June.
    See citations under working paper version above.
  4. Hector F. Calvo-Pardo & Tullio Mancini & Jose Olmo, 2022. "Machine Learning the Carbon Footprint of Bitcoin Mining," JRFM, MDPI, vol. 15(2), pages 1-30, February.
    See citations under working paper version above.
  5. Burcu Kapar & Jose Olmo, 2021. "Analysis of Bitcoin prices using market and sentiment variables," The World Economy, Wiley Blackwell, vol. 44(1), pages 45-63, January.

    Cited by:

    1. Peter Fratrič & Giovanni Sileno & Sander Klous & Tom Engers, 2022. "Manipulation of the Bitcoin market: an agent-based study," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-29, December.
    2. Hajek, Petr & Hikkerova, Lubica & Sahut, Jean-Michel, 2023. "How well do investor sentiment and ensemble learning predict Bitcoin prices?," Research in International Business and Finance, Elsevier, vol. 64(C).
    3. Adel Benhamed & Ahlem Selma Messai & Ghassen El Montasser, 2023. "On the Determinants of Bitcoin Returns and Volatility: What We Get from Gets?," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    4. Dibooglu, Sel & Cevik, Emrah I. & Gillman, Max, 2022. "Gold, silver, and the US dollar as harbingers of financial calm and distress," The Quarterly Review of Economics and Finance, Elsevier, vol. 86(C), pages 200-210.
    5. Dias, Ishanka K. & Fernando, J.M. Ruwani & Fernando, P. Narada D., 2022. "Does investor sentiment predict bitcoin return and volatility? A quantile regression approach," International Review of Financial Analysis, Elsevier, vol. 84(C).
    6. Balcilar, Mehmet & Ozdemir, Huseyin & Agan, Busra, 2022. "Effects of COVID-19 on cryptocurrency and emerging market connectedness: Empirical evidence from quantile, frequency, and lasso networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    7. Bhuiyan, Rubaiyat Ahsan & Husain, Afzol & Zhang, Changyong, 2021. "A wavelet approach for causal relationship between bitcoin and conventional asset classes," Resources Policy, Elsevier, vol. 71(C).
    8. Joseph J. French, 2021. "#Bitcoin, #COVID-19: Twitter-Based Uncertainty and Bitcoin Before and during the Pandemic," IJFS, MDPI, vol. 9(2), pages 1-7, May.
    9. Gaies, Brahim & Chaâbane, Najeh & Bouzouita, Nesrine, 2024. "Navigating the storm: Time-frequency quantile dependence and non-linear causality between crypto-currency market volatility and financial instability," The Quarterly Review of Economics and Finance, Elsevier, vol. 93(C), pages 43-70.
    10. A. V. Biju & Aparna Merin Mathew & P. P. Nithi Krishna & M. P. Akhil, 2022. "Is the future of bitcoin safe? A triangulation approach in the reality of BTC market through a sentiments analysis," Digital Finance, Springer, vol. 4(4), pages 275-290, December.
    11. Ahmed, Walid M.A., 2022. "Robust drivers of Bitcoin price movements: An extreme bounds analysis," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).

  6. Maria Kyriacou & Jose Olmo & Marius Strittmatter, 2021. "Optimal portfolio allocation using option‐implied information," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(2), pages 266-285, February.

    Cited by:

    1. Massimo Guidolin & Kai Wang, 2022. "The Empirical Performance of Option Implied Volatility Surface-Driven Optimal Portfolios," BAFFI CAREFIN Working Papers 22190, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.

  7. Ricardo Laborda & Jose Olmo, 2021. "An Empirical Analysis of Terrorism and Stock Market Spillovers: The Case of Spain," Defence and Peace Economics, Taylor & Francis Journals, vol. 32(1), pages 68-86, January.

    Cited by:

    1. Bouri, Elie & Hammoud, Rami & Kassm, Christina Abou, 2023. "The effect of oil implied volatility and geopolitical risk on GCC stock sectors under various market conditions," Energy Economics, Elsevier, vol. 120(C).

  8. Laborda, Ricardo & Olmo, Jose, 2021. "Volatility spillover between economic sectors in financial crisis prediction: Evidence spanning the great financial crisis and Covid-19 pandemic," Research in International Business and Finance, Elsevier, vol. 57(C).

    Cited by:

    1. Choi, Sun-Yong, 2022. "Dynamic volatility spillovers between industries in the US stock market: Evidence from the COVID-19 pandemic and Black Monday," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    2. Jana, Rabin K & Ghosh, Indranil & Goyal, Vinay, 2022. "Spillover nexus of financial stress during black Swan events," Finance Research Letters, Elsevier, vol. 48(C).
    3. Hasan, Md. Bokhtiar & Mahi, Masnun & Hassan, M. Kabir & Bhuiyan, Abul Bashar, 2021. "Impact of COVID-19 pandemic on stock markets: Conventional vs. Islamic indices using wavelet-based multi-timescales analysis," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    4. Dai, Zhifeng & Peng, Yongxin, 2022. "Economic policy uncertainty and stock market sector time-varying spillover effect: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    5. Jeonghwa Cha & Kyungbo Park & Hangook Kim & Jongyi Hong, 2023. "Crisis Index Prediction Based on Momentum Theory and Earnings Downside Risk Theory: Focusing on South Korea’s Energy Industry," Energies, MDPI, vol. 16(5), pages 1-20, February.
    6. Tam Hoang-Nhat Dang & Nhan Thien Nguyen & Duc Hong Vo, 2023. "Sectoral volatility spillovers and their determinants in Vietnam," Economic Change and Restructuring, Springer, vol. 56(1), pages 681-700, February.
    7. Dorota Zebrowska-Suchodolska & Andrzej Karpio & Krzysztof Kompa, 2021. "COVID-19 Pandemic: Stock Markets Situation in European Ex-Communist Countries," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 1106-1128.
    8. Hernandez, Jose Arreola & Shahzad, Syed Jawad Hussain & Sadorsky, Perry & Uddin, Gazi Salah & Bouri, Elie & Kang, Sang Hoon, 2022. "Regime specific spillovers across US sectors and the role of oil price volatility," Energy Economics, Elsevier, vol. 107(C).
    9. Constantin Anghelache & Madalina Gabriela Anghel & Stefan Virgil Iacob, 2022. "The Social - Economic State Of Romania Under The Impact Of Crisis," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 4, pages 29-39, August.
    10. Md. Bokhtiar Hasan & Masnun Mahi & Tapan Sarker & Md. Ruhul Amin, 2021. "Spillovers of the COVID-19 Pandemic: Impact on Global Economic Activity, the Stock Market, and the Energy Sector," JRFM, MDPI, vol. 14(5), pages 1-18, May.
    11. Hasan Fehmi Baklaci & Tezer Yelkenci, 2022. "Cross-time-frequency analysis of volatility linkages in global currency markets: an extended framework," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 12(2), pages 267-314, June.
    12. Pham, Son Duy & Nguyen, Thao Thac Thanh & Do, Hung Xuan & Vo, Xuan Vinh, 2023. "Portfolio diversification during the COVID-19 pandemic: Do vaccinations matter?," Journal of Financial Stability, Elsevier, vol. 65(C).
    13. Thai Hung, Ngo & Nguyen, Linh Thi My & Vinh Vo, Xuan, 2022. "Exchange rate volatility connectedness during Covid-19 outbreak: DECO-GARCH and Transfer Entropy approaches," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    14. Mensi, Walid & Vo, Xuan Vinh & Ko, Hee-Un & Kang, Sang Hoon, 2023. "Frequency spillovers between green bonds, global factors and stock market before and during COVID-19 crisis," Economic Analysis and Policy, Elsevier, vol. 77(C), pages 558-580.
    15. Tabak, Benjamin Miranda & Silva, Igor Bettanin Dalla Riva e & Silva, Thiago Christiano, 2022. "Analysis of connectivity between the world’s banking markets: The COVID-19 global pandemic shock," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 324-336.
    16. Gofran, Ruhana Zareen & Gregoriou, Andros & Haar, Lawrence, 2022. "Impact of Coronavirus on liquidity in financial markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 78(C).
    17. Sharma, Gagan Deep & Shahbaz, Muhammad & Singh, Sanjeet & Chopra, Ritika & Cifuentes-Faura, Javier, 2023. "Investigating the nexus between green economy, sustainability, bitcoin and oil prices: Contextual evidence from the United States," Resources Policy, Elsevier, vol. 80(C).
    18. Umar, Zaghum & Polat, Onur & Choi, Sun-Yong & Teplova, Tamara, 2022. "Dynamic connectedness between non-fungible tokens, decentralized finance, and conventional financial assets in a time-frequency framework," Pacific-Basin Finance Journal, Elsevier, vol. 76(C).
    19. Kapar, Burcu & Billah, Syed Mabruk & Rana, Faisal & Balli, Faruk, 2024. "An investigation of the frequency dynamics of spillovers and connectedness among GCC sectoral indices," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 1442-1467.
    20. Al-Nassar, Nassar S. & Yousaf, Imran & Makram, Beljid, 2023. "Spillovers between positively and negatively affected service sectors from the COVID-19 health crisis: Implications for portfolio management," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
    21. Ahmad, Wasim & Hernandez, Jose Arreola & Saini, Seema & Mishra, Ritesh Kumar, 2021. "The US equity sectors, implied volatilities, and COVID-19: What does the spillover analysis reveal?," Resources Policy, Elsevier, vol. 72(C).
    22. Bouteska, Ahmed & Hajek, Petr & Fisher, Ben & Abedin, Mohammad Zoynul, 2023. "Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network," Research in International Business and Finance, Elsevier, vol. 64(C).
    23. Zhang, Yi & Zhou, Long & Chen, Yajiao & Liu, Fang, 2022. "The contagion effect of jump risk across Asian stock markets during the Covid-19 pandemic," The North American Journal of Economics and Finance, Elsevier, vol. 61(C).
    24. Ahmed, Shamima & Banerjee, Ameet Kumar & James, Wendy & Moussa, Faten, 2024. "Is the Evergrande crisis spilling beyond China?," Research in International Business and Finance, Elsevier, vol. 67(PB).

  9. Jose Olmo, 2021. "Optimal portfolio allocation and asset centrality revisited," Quantitative Finance, Taylor & Francis Journals, vol. 21(9), pages 1475-1490, September.

    Cited by:

    1. Jilber Urbina & Miguel Santolino & Montserrat Guillen, 2021. "Covariance Principle for Capital Allocation: A Time-Varying Approach," Mathematics, MDPI, vol. 9(16), pages 1-13, August.
    2. Christis Katsouris, 2021. "Optimal Portfolio Choice and Stock Centrality for Tail Risk Events," Papers 2112.12031, arXiv.org.
    3. Christis Katsouris, 2023. "Statistical Estimation for Covariance Structures with Tail Estimates using Nodewise Quantile Predictive Regression Models," Papers 2305.11282, arXiv.org, revised Jul 2023.

  10. Calvo-Pardo, Hector & Mancini, Tullio & Olmo, Jose, 2021. "Granger causality detection in high-dimensional systems using feedforward neural networks," International Journal of Forecasting, Elsevier, vol. 37(2), pages 920-940.

    Cited by:

    1. Christis Katsouris, 2021. "Optimal Portfolio Choice and Stock Centrality for Tail Risk Events," Papers 2112.12031, arXiv.org.
    2. Bouteska, Ahmed & Harasheh, Murad & Abedin, Mohammad Zoynul, 2023. "Revisiting overconfidence in investment decision-making: Further evidence from the U.S. market," Research in International Business and Finance, Elsevier, vol. 66(C).
    3. Esra Alp Coşkun & Hakan Kahyaoglu & Chi Keung Marco Lau, 2023. "Which return regime induces overconfidence behavior? Artificial intelligence and a nonlinear approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-34, December.

  11. Cheang, Chi Wan & Olmo, Jose & Ma, Tiejun & Sung, Ming-Chien & McGroarty, Frank, 2020. "Optimal asset allocation using a combination of implied and historical information," International Review of Financial Analysis, Elsevier, vol. 67(C).

    Cited by:

    1. Yi Huang & Wei Zhu & Duan Li & Shushang Zhu & Shikun Wang, 2023. "Integrating Different Informations for Portfolio Selection," Papers 2305.17881, arXiv.org.

  12. Kapar, Burcu & Olmo, Jose & Ghalayini, Rim, 2020. "Financial integration in the United Arab Emirates Stock Markets," Finance Research Letters, Elsevier, vol. 33(C).

    Cited by:

    1. Hsiang-Hsi Liu & Chien-Kuo Tseng, 2022. "Common Components in Co-integrated System and Its Estimation and Application: Evidence from Five Stock Markets in Asia-Pacific Chinese Region," Bulletin of Applied Economics, Risk Market Journals, vol. 9(2), pages 101-121.
    2. Kapar, Burcu & Billah, Syed Mabruk & Rana, Faisal & Balli, Faruk, 2024. "An investigation of the frequency dynamics of spillovers and connectedness among GCC sectoral indices," International Review of Economics & Finance, Elsevier, vol. 89(PA), pages 1442-1467.
    3. Ngo Thai Hung, 2021. "Financial connectedness of GCC emerging stock markets," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(4), pages 753-773, December.

  13. Kapar, Burcu & Olmo, Jose, 2019. "An analysis of price discovery between Bitcoin futures and spot markets," Economics Letters, Elsevier, vol. 174(C), pages 62-64.

    Cited by:

    1. Huang, Yingying & Duan, Kun & Urquhart, Andrew, 2023. "Time-varying dependence between Bitcoin and green financial assets: A comparison between pre- and post-COVID-19 periods," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
    2. Carl Luft & Jin Man Lee & Jin W. Choi, 2019. "“Chicago Mercantile Exchange Bitcoin Futures: Volatility, Liquidity and Margin”," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 69(3), pages 55-74, July-Sept.
    3. Lin, Mei-Yin & An, Che-Lun, 2021. "The relationship between Bitcoin and resource commodity futures: Evidence from NARDL approach," Resources Policy, Elsevier, vol. 74(C).
    4. Shimeng Shi, 2022. "Bitcoin futures risk premia," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(12), pages 2190-2217, December.
    5. Bao Doan & Huy Pham & Binh Nguyen Thanh, 2022. "Price discovery in the cryptocurrency market: evidence from institutional activity," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 49(1), pages 111-131, March.
    6. Julien Chevallier & Dominique Guégan & Stéphane Goutte, 2021. "Is It Possible to Forecast the Price of Bitcoin?," Post-Print halshs-04250269, HAL.
    7. Wang, Qiyu & Chong, Terence Tai-Leung, 2021. "Factor pricing of cryptocurrencies," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    8. Ruan, Qingsong & Meng, Lu & Lv, Dayong, 2021. "Effect of introducing Bitcoin futures on the underlying Bitcoin market efficiency: A multifractal analysis," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    9. Conlon, Thomas & Corbet, Shaen & McGee, Richard J., 2024. "The Bitcoin volume-volatility relationship: A high frequency analysis of futures and spot exchanges," International Review of Financial Analysis, Elsevier, vol. 91(C).
    10. Parthajit Kayal & Purnima Rohilla, 2021. "Bitcoin in the economics and finance literature: a survey," SN Business & Economics, Springer, vol. 1(7), pages 1-21, July.
    11. Prashant Sharma & Prashant Gupta & Dinesh Kumar Sharma & Gaurav Agarwal, 2022. "Investigating the Efficiency of Bitcoin Futures in Price Discovery," International Journal of Economics and Financial Issues, Econjournals, vol. 12(3), pages 104-109, May.
    12. Yang Hu & Yang (Greg) Hou & Les Oxley, 2019. "Spot and Futures Prices of Bitcoin: Causality, Cointegration and Price Discovery from a Time-Varying Perspective," Working Papers in Economics 19/13, University of Waikato.
    13. Oliver Entrop & Bart Frijns & Marco Seruset, 2020. "The determinants of price discovery on bitcoin markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(5), pages 816-837, May.
    14. Liu, Ruozhou & Wan, Shanfeng & Zhang, Zili & Zhao, Xuejun, 2020. "Is the introduction of futures responsible for the crash of Bitcoin?," Finance Research Letters, Elsevier, vol. 34(C).
    15. Jun Deng & Huifeng Pan & Shuyu Zhang & Bin Zou, 2021. "Optimal Bitcoin trading with inverse futures," Annals of Operations Research, Springer, vol. 304(1), pages 139-163, September.
    16. Fassas, Athanasios P., 2021. "Price discovery in US money market benchmarks: LIBOR vs. SOFR," Economics Letters, Elsevier, vol. 204(C).
    17. Sebastião, Helder & Godinho, Pedro, 2020. "Bitcoin futures: An effective tool for hedging cryptocurrencies," Finance Research Letters, Elsevier, vol. 33(C).
    18. Shynkevich, Andrei, 2021. "Bitcoin arbitrage," Finance Research Letters, Elsevier, vol. 40(C).
    19. Ke Xu & Yu‐Lun Chen & Bo Liu & Jian Chen, 2024. "Price discovery and long‐memory property: Simulation and empirical evidence from the bitcoin market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(4), pages 605-618, April.
    20. Virginie Terraza & Aslı Boru İpek & Mohammad Mahdi Rounaghi, 2024. "The nexus between the volatility of Bitcoin, gold, and American stock markets during the COVID-19 pandemic: evidence from VAR-DCC-EGARCH and ANN models," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-34, December.
    21. Alexander, Carol & Heck, Daniel F., 2020. "Price discovery in Bitcoin: The impact of unregulated markets," Journal of Financial Stability, Elsevier, vol. 50(C).
    22. Helder Miguel Correia Virtuoso Sebastião & Paulo José Osório Rupino Da Cunha & Pedro Manuel Cortesão Godinho, 2021. "Cryptocurrencies and blockchain. Overview and future perspectives," International Journal of Economics and Business Research, Inderscience Enterprises Ltd, vol. 21(3), pages 305-342.
    23. Umar, Muhammad & Rizvi, Syed Kumail Abbas & Naqvi, Bushra, 2021. "Dance with the devil? The nexus of fourth industrial revolution, technological financial products and volatility spillovers in global financial system," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    24. Ma, Yechi & Ahmad, Ferhana & Liu, Miao & Wang, Zilong, 2020. "Portfolio optimization in the era of digital financialization using cryptocurrencies," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    25. Panpan Zhu & Xing Zhang & You Wu & Hao Zheng & Yinpeng Zhang, 2021. "Investor attention and cryptocurrency: Evidence from the Bitcoin market," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-28, February.
    26. Wang, Jying-Nan & Liu, Hung-Chun & Hsu, Yuan-Teng, 2020. "Time-of-day periodicities of trading volume and volatility in Bitcoin exchange: Does the stock market matter?," Finance Research Letters, Elsevier, vol. 34(C).
    27. Chen, Yu-Lun & Chang, Yung Ting & Yang, J. Jimmy, 2023. "Cryptocurrency hacking incidents and the price dynamics of Bitcoin spot and futures," Finance Research Letters, Elsevier, vol. 55(PB).
    28. Yakup Söylemez, 2019. "Cryptocurrency Derivatives: The Case of Bitcoin," Contributions to Economics, in: Umit Hacioglu (ed.), Blockchain Economics and Financial Market Innovation, chapter 0, pages 515-530, Springer.
    29. Zhao, Xin & Ghaemi Asl, Mahdi & Rashidi, Muhammad Mahdi & Vasa, László & Shahzad, Umer, 2023. "Interoperability of the revolutionary blockchain architectures and Islamic and conventional technology markets: Case of Metaverse, HPB, and Bloknet," The Quarterly Review of Economics and Finance, Elsevier, vol. 92(C), pages 112-131.
    30. Aurelio F. Bariviera & Ignasi Merediz‐Solà, 2021. "Where Do We Stand In Cryptocurrencies Economic Research? A Survey Based On Hybrid Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 377-407, April.
    31. Hu, Yang & Hou, Yang Greg & Oxley, Les, 2020. "What role do futures markets play in Bitcoin pricing? Causality, cointegration and price discovery from a time-varying perspective?," International Review of Financial Analysis, Elsevier, vol. 72(C).
    32. Pati, Pratap Chandra, 2022. "Informativeness of CME Micro Bitcoin Futures in Pricing of Bitcoin: Intraday Evidence," Finance Research Letters, Elsevier, vol. 49(C).
    33. Ali, Fahad & Khurram, Muhammad Usman & Sensoy, Ahmet & Vo, Xuan Vinh, 2024. "Green cryptocurrencies and portfolio diversification in the era of greener paths," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    34. Fassas, Athanasios P. & Papadamou, Stephanos & Koulis, Alexandros, 2020. "Price discovery in bitcoin futures," Research in International Business and Finance, Elsevier, vol. 52(C).
    35. Efe Caglar Cagli & Pinar Evrim Mandaci, 2021. "Information transmission between bitcoin derivatives and spot markets: high-frequency causality analysis with Fourier approximation," Economics and Business Letters, Oviedo University Press, vol. 10(4), pages 394-402.
    36. Hung, Jui-Cheng & Liu, Hung-Chun & Yang, J. Jimmy, 2021. "Trading activity and price discovery in Bitcoin futures markets," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 107-120.
    37. Gil-Alana, Luis Alberiko & Abakah, Emmanuel Joel Aikins & Rojo, María Fátima Romero, 2020. "Cryptocurrencies and stock market indices. Are they related?," Research in International Business and Finance, Elsevier, vol. 51(C).
    38. Alexander, Carol & Choi, Jaehyuk & Massie, Hamish R.A. & Sohn, Sungbin, 2020. "Price discovery and microstructure in ether spot and derivative markets," International Review of Financial Analysis, Elsevier, vol. 71(C).
    39. Borri, Nicola & Shakhnov, Kirill, 2023. "Cryptomarket discounts," Journal of International Money and Finance, Elsevier, vol. 139(C).
    40. Rahul Kumar Singh, 2023. "Efficiency of Wheat Futures across APMC Mandis," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(3), pages 681-701, September.
    41. Gemayel, Roland & Franus, Tatiana & Bowden, James, 2023. "Price discovery between Bitcoin spot markets and exchange traded products," Economics Letters, Elsevier, vol. 228(C).
    42. Shimeng Shi & Yukun Shi, 2021. "Bitcoin futures: trade it or ban it?," The European Journal of Finance, Taylor & Francis Journals, vol. 27(4-5), pages 381-396, March.
    43. Jinghong Wu & Ke Xu & Xinwei Zheng & Jian Chen, 2021. "Fractional cointegration in bitcoin spot and futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(9), pages 1478-1494, September.
    44. Dimpfl, Thomas & Peter, Franziska J., 2021. "Nothing but noise? Price discovery across cryptocurrency exchanges," Journal of Financial Markets, Elsevier, vol. 54(C).
    45. Hattori, Takahiro & Ishida, Ryo, 2021. "Did the introduction of Bitcoin futures crash the Bitcoin market at the end of 2017?," The North American Journal of Economics and Finance, Elsevier, vol. 56(C).
    46. Yu‐Lun Chen & J. Jimmy Yang, 2024. "Time‐varying price discovery in regular and microbitcoin futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(1), pages 103-121, January.
    47. Domingo, Ribeiro-Soriano & Piñeiro-Chousa, Juan & Ángeles López-Cabarcos, M., 2020. "What factors drive returns on initial coin offerings?," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    48. Lee, Seungho & Meslmani, Nabil El & Switzer, Lorne N., 2020. "Pricing Efficiency and Arbitrage in the Bitcoin Spot and Futures Markets," Research in International Business and Finance, Elsevier, vol. 53(C).
    49. John W Goodell & Stéphane Goutte, 2020. "Diversifying with cryptocurrencies during COVID-19," Working Papers halshs-02876529, HAL.
    50. Constandina Koki & Stefanos Leonardos & Georgios Piliouras, 2019. "A Peek into the Unobservable: Hidden States and Bayesian Inference for the Bitcoin and Ether Price Series," Papers 1909.10957, arXiv.org, revised Jul 2021.
    51. Cevik, Emrah Ismail & Gunay, Samet & Dibooglu, Sel & Yıldırım, Durmuş Çağrı, 2023. "The impact of expected and unexpected events on Bitcoin price development: Introduction of futures market and COVID-19," Finance Research Letters, Elsevier, vol. 54(C).

  14. Matthew Lyon & Jose Olmo, 2018. "Does the PPP condition hold for oil†exporting countries? A quantile cointegration regression approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 23(2), pages 79-93, April.

    Cited by:

    1. Mudeer A. Khattak & Buerhan Saiti & Shabeer Khan, 2023. "Does market power explain margins in dual banking? Evidence from panel quantile regression," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(2), pages 1826-1844, April.

  15. Antonio F Galvao & Ted Juhl & Gabriel Montes-Rojas & Jose Olmo, 2018. "Testing Slope Homogeneity in Quantile Regression Panel Data with an Application to the Cross-Section of Stock Returns," Journal of Financial Econometrics, Oxford University Press, vol. 16(2), pages 211-243.

    Cited by:

    1. Yiren Wang & Liangjun Su & Yichong Zhang, 2022. "Low-rank Panel Quantile Regression: Estimation and Inference," Papers 2210.11062, arXiv.org.
    2. Chuliá, Helena & Koser, Christoph & Uribe, Jorge M., 2021. "Analyzing the Nonlinear Pricing of Liquidity Risk according to the Market State," Finance Research Letters, Elsevier, vol. 38(C).
    3. Daan Opschoor & Dick van Dijk & Philip Hans Franses, 2021. "Heterogeneity in Manufacturing Growth Risk," Tinbergen Institute Discussion Papers 21-036/III, Tinbergen Institute.

  16. Antonio F. Galvao & Gabriel Montes–Rojas & Jose Olmo & Suyong Song, 2018. "On solving endogeneity with invalid instruments: an application to investment equations," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 689-716, June.

    Cited by:

    1. Ali Al-Sharadqah & Majid Mojirsheibani & William Pouliot, 2020. "On the performance of weighted bootstrapped kernel deconvolution density estimators," Statistical Papers, Springer, vol. 61(4), pages 1773-1798, August.

  17. M. Angeles Carnero & Jose Olmo & Lorenzo Pascual, 2018. "Modelling the Dynamics of Fuel and EU Allowance Prices during Phase 3 of the EU ETS," Energies, MDPI, vol. 11(11), pages 1-23, November.

    Cited by:

    1. Joao Leitao & Joaquim Ferreira & Ernesto Santibanez‐Gonzalez, 2021. "Green bonds, sustainable development and environmental policy in the European Union carbon market," Business Strategy and the Environment, Wiley Blackwell, vol. 30(4), pages 2077-2090, May.
    2. Qiao, Sen & Dang, Yi Jing & Ren, Zheng Yu & Zhang, Kai Quan, 2023. "The dynamic spillovers among carbon, fossil energy and electricity markets based on a TVP-VAR-SV method," Energy, Elsevier, vol. 266(C).
    3. Xing Zhang & Chongchong Zhang & Zhuoqun Wei, 2019. "Carbon Price Forecasting Based on Multi-Resolution Singular Value Decomposition and Extreme Learning Machine Optimized by the Moth–Flame Optimization Algorithm Considering Energy and Economic Factors," Energies, MDPI, vol. 12(22), pages 1-23, November.
    4. Friedrich, Marina & Mauer, Eva-Maria & Pahle, Michael & Tietjen, Oliver, 2020. "From fundamentals to financial assets: the evolution of understanding price formation in the EU ETS," EconStor Preprints 225210, ZBW - Leibniz Information Centre for Economics.
    5. Nader Trabelsi & Aviral Kumar Tiwari, 2023. "CO2 Emission Allowances Risk Prediction with GAS and GARCH Models," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 775-805, February.
    6. Vlad-Cosmin Bulai & Alexandra Horobet & Oana Cristina Popovici & Lucian Belascu & Sofia Adriana Dumitrescu, 2021. "A VaR-Based Methodology for Assessing Carbon Price Risk across European Union Economic Sectors," Energies, MDPI, vol. 14(24), pages 1-21, December.

  18. Jose Olmo & Marcos Sanso-Navarro, 2018. "Unconventional monetary policies and the credit market," International Journal of Monetary Economics and Finance, Inderscience Enterprises Ltd, vol. 11(5), pages 480-498.

    Cited by:

    1. Rui Wang, 2021. "Evaluating the Unconventional Monetary Policy of the Bank of Japan: A DSGE Approach," JRFM, MDPI, vol. 14(6), pages 1-18, June.

  19. Laborda, Ricardo & Olmo, Jose, 2017. "Optimal asset allocation for strategic investors," International Journal of Forecasting, Elsevier, vol. 33(4), pages 970-987.

    Cited by:

    1. Reza Bradrania & Davood Pirayesh Neghab, 2022. "State-dependent Asset Allocation Using Neural Networks," Papers 2211.00871, arXiv.org.
    2. Bradrania, Reza & Pirayesh Neghab, Davood, 2021. "State-dependent asset allocation using neural networks," MPRA Paper 115254, University Library of Munich, Germany.

  20. Juan Laborda & Ricardo Laborda & Jose Olmo, 2016. "Investing in the size factor," Quantitative Finance, Taylor & Francis Journals, vol. 16(1), pages 85-100, January.

    Cited by:

    1. Laborda, Ricardo & Olmo, Jose, 2017. "Optimal asset allocation for strategic investors," International Journal of Forecasting, Elsevier, vol. 33(4), pages 970-987.
    2. Choi, Jungjun & Yang, Xiye, 2022. "Asymptotic properties of correlation-based principal component analysis," Journal of Econometrics, Elsevier, vol. 229(1), pages 1-18.

  21. Ana-Maria Fuertes & Jose Olmo, 2016. "On Setting Day-Ahead Equity Trading Risk Limits: VaR Prediction at Market Close or Open?," JRFM, MDPI, vol. 9(3), pages 1-20, September.

    Cited by:

    1. Jayawardena, Nirodha I. & Todorova, Neda & Li, Bin & Su, Jen-Je, 2020. "Volatility forecasting using related markets’ information for the Tokyo stock exchange," Economic Modelling, Elsevier, vol. 90(C), pages 143-158.
    2. Antonio Díaz & Gonzalo García-Donato & Andrés Mora-Valencia, 2019. "Quantifying Risk in Traditional Energy and Sustainable Investments," Sustainability, MDPI, vol. 11(3), pages 1-22, January.

  22. Katja Ahoniemi & Ana-Maria Fuertes & Jose Olmo, 2016. "Overnight News and Daily Equity Trading Risk Limits," Journal of Financial Econometrics, Oxford University Press, vol. 14(3), pages 525-551.

    Cited by:

    1. Jayawardena, Nirodha I. & Todorova, Neda & Li, Bin & Su, Jen-Je, 2020. "Volatility forecasting using related markets’ information for the Tokyo stock exchange," Economic Modelling, Elsevier, vol. 90(C), pages 143-158.
    2. Barbara Będowska-Sójka, 2018. "Is intraday data useful for forecasting VaR? The evidence from EUR/PLN exchange rate," Risk Management, Palgrave Macmillan, vol. 20(4), pages 326-346, November.
    3. Chun, Dohyun & Cho, Hoon & Ryu, Doojin, 2023. "Discovering the drivers of stock market volatility in a data-rich world," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).
    4. Dohyun Chun & Donggyu Kim, 2021. "State Heterogeneity Analysis of Financial Volatility Using High-Frequency Financial Data," Papers 2102.13404, arXiv.org.
    5. Dohyun Chun & Donggyu Kim, 2022. "State Heterogeneity Analysis of Financial Volatility using high‐frequency Financial Data," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(1), pages 105-124, January.

  23. Rafael Gonz�lez-Val & Jose Olmo, 2015. "Growth in a Cross-section of Cities: Location, Increasing Returns or Random Growth?," Spatial Economic Analysis, Taylor & Francis Journals, vol. 10(2), pages 230-261, June.
    See citations under working paper version above.
  24. Olmo, Jose & Sanso-Navarro, Marcos, 2015. "Changes in the transmission of monetary policy during crisis episodes: Evidence from the euro area and the U.S," Economic Modelling, Elsevier, vol. 48(C), pages 155-166.

    Cited by:

    1. Liu, Dayu & Xu, Ning & Zhao, Tingting & Song, Yang, 2018. "Identifying the nonlinear correlation between business cycle and monetary policy rule: Evidence from China and the U.S," Economic Modelling, Elsevier, vol. 73(C), pages 45-54.
    2. Massimo Guidolin & Manuela Pedio, 2019. "Does the Cost of Private Debt Respond to Monetary Policy? Heteroskedasticity-Based Identification in a Model with Regimes," BAFFI CAREFIN Working Papers 19118, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    3. Chikashi Tsuji, 2016. "Did the expectations channel work? Evidence from quantitative easing in Japan, 2001–06," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1210996-121, December.
    4. Hummaira Jabeen, 2022. "Monetary Policy Shock Transmission in Emerging Markets," Journal of Policy Research (JPR), Research Foundation for Humanity (RFH), vol. 8(4), pages 379-390, December.
    5. Juan S. Holguín & Jorge M. Uribe, 2020. "The credit supply channel of monetary policy: evidence from a FAVAR model with sign restrictions," Empirical Economics, Springer, vol. 59(5), pages 2443-2472, November.
    6. Frijters, Paul & Antić, Nemanja, 2016. "Can collapsing business networks explain economic downturns?," Economic Modelling, Elsevier, vol. 54(C), pages 289-308.

  25. Iori Giulia & Kapar Burcu & Olmo Jose, 2015. "Bank characteristics and the interbank money market: a distributional approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 19(3), pages 249-283, June.

    Cited by:

    1. Green, Christopher & Bai, Ye & Murinde, Victor & Ngoka, Kethi & Maana, Isaya & Tiriongo, Samuel, 2016. "Overnight interbank markets and the determination of the interbank rate: A selective survey," International Review of Financial Analysis, Elsevier, vol. 44(C), pages 149-161.
    2. Paolo Barucca & Fabrizio Lillo, 2018. "The organization of the interbank network and how ECB unconventional measures affected the e-MID overnight market," Computational Management Science, Springer, vol. 15(1), pages 33-53, January.
    3. Domenico Di Gangi & Giacomo Bormetti & Fabrizio Lillo, 2022. "Score Driven Generalized Fitness Model for Sparse and Weighted Temporal Networks," Papers 2202.09854, arXiv.org, revised Mar 2022.
    4. Temizsoy, Asena & Iori, Giulia & Montes-Rojas, Gabriel, 2015. "The role of bank relationships in the interbank market," Journal of Economic Dynamics and Control, Elsevier, vol. 59(C), pages 118-141.
    5. Berardi, Simone & Tedeschi, Gabriele, 2017. "From banks' strategies to financial (in)stability," International Review of Economics & Finance, Elsevier, vol. 47(C), pages 255-272.
    6. Katarzyna Bech & Grant Hillier, 2015. "Nonparametric testing for exogeneity with discrete regressors and instruments," CeMMAP working papers 11/15, Institute for Fiscal Studies.
    7. Bednarek, Peter & Dinger, Valeriya & Schultz, Alison & von Westernhagen, Natalja, 2023. "Banks of a feather: The informational advantage of being alike," Discussion Papers 09/2023, Deutsche Bundesbank.
    8. Paolo Barucca & Fabrizio Lillo, 2015. "The organization of the interbank network and how ECB unconventional measures affected the e-MID overnight market," Papers 1511.08068, arXiv.org, revised Sep 2017.
    9. Anastasios Demertzidis, 2019. "Interbank transactions on the intraday frequency: -Different market states and the effects of the financial crisis-," MAGKS Papers on Economics 201932, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).

  26. Laborda, Ricardo & Olmo, Jose, 2014. "Investor sentiment and bond risk premia," Journal of Financial Markets, Elsevier, vol. 18(C), pages 206-233.

    Cited by:

    1. Alex Edmans & Adrian Fernandez-Perez & Alexandre Garel & Ivan Indriawan, 2021. "Music Sentiment and Stock Returns Around the World," Post-Print hal-03324805, HAL.
    2. Xu, Alan, 2022. "Air pollution and mediation effects in stock market, longitudinal evidence from China," International Review of Financial Analysis, Elsevier, vol. 83(C).
    3. Elie Bouri & Rangan Gupta & Anandamayee Majumdar & Sowmya Subramaniam, 2020. "Time-Varying Risk Aversion and Forecastability of the US Term Structure of Interest Rates," Working Papers 202098, University of Pretoria, Department of Economics.
    4. Juan Andrés Espinosa-Torres & Jose E. Gomez-Gonzalez & Luis Fernando Melo-Velandia & José Fernando Moreno-Gutiérrez, 2015. "The International Transmission of Risk: Causal Relations Among Developed and Emerging Countries’ Term Premia," Borradores de Economia 869, Banco de la Republica de Colombia.
    5. Roland Füss & Massimo Guidolin & Christian Koeppel, 2019. "Sentiment Risk Premia In The Cross-Section of Global Equity," Working Papers on Finance 1913, University of St. Gallen, School of Finance, revised May 2020.
    6. Juan Andrés Espinosa Torres & Luis Fernando Melo Velandia & José Fernando Moreno Gutiérrez, 2014. "Estimación de la prima por vencimiento de los TES en pesos del gobierno colombiano," Borradores de Economia 854, Banco de la Republica de Colombia.
    7. Zhou, Liyun & Huang, Jialiang, 2020. "Contagion of future-level sentiment in Chinese Agricultural Futures Markets," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    8. Turkmen Muldur Gozde & Kandir Serkan Yılmaz & Onal Yıldırım Beyazıt, 2019. "Investor Sentiment and Speculative Bond Yield Spreads," Foundations of Management, Sciendo, vol. 11(1), pages 177-186, January.
    9. Zhou, Liyun & Huang, Jialiang, 2020. "Excess co-movement of agricultural futures prices: Perspective from contagious investor sentiment," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    10. Mehmet Balcilar & Rangan Gupta & Shixuan Wang & Mark E. Wohar, 2019. "Oil Price Uncertainty and Movements in the US Government Bond Risk Premia," Working Papers 201919, University of Pretoria, Department of Economics.
    11. Louis Raffestin, 2017. "Do bond credit ratings lead to excess comovement?," Post-Print hal-01649992, HAL.
    12. Chi-Wei Su & Xu-Yu Cai & Ran Tao, 2020. "Can Stock Investor Sentiment Be Contagious in China?," Sustainability, MDPI, vol. 12(4), pages 1-16, February.
    13. Oguzhan Cepni & Rangan Gupta & Mark E. Wohar, 2019. "Variants of Consumption-Wealth Ratios and Predictability of U.S. Government Bond Risk Premia: Old is still Gold," Working Papers 201912, University of Pretoria, Department of Economics.
    14. Dan Zhang & Arash Farnoosh & Zhengwei Ma, 2022. "Does the Launch of Shanghai Crude Oil Futures Stabilize the Spot Market ? A Financial Cycle Perspective," Post-Print hal-03910474, HAL.
    15. Wei Zhang & Yingxiu Zhao & Pengfei Wang & Dehua Shen, 2020. "Investor Sentiment and the Return Rate of P2P Lending Platform," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 27(1), pages 97-113, March.
    16. Çepni, Oğguzhan & Demirer, Riza & Gupta, Rangan & Pierdzioch, Christian, 2020. "Time-varying risk aversion and the predictability of bond premia," Finance Research Letters, Elsevier, vol. 34(C).
    17. Chen, Wen-Yi & Chen, Mei-Ping, 2022. "Twitter’s daily happiness sentiment, economic policy uncertainty, and stock index fluctuations," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).
    18. Manish K. Singh & Marta Gómez-Puig & Simón Sosvilla-Rivero, 2019. "“Increasing contingent guarantees: The asymmetrical effect on sovereign risk of different government interventions"," IREA Working Papers 201914, University of Barcelona, Research Institute of Applied Economics, revised Sep 2019.
    19. Agoraki, Maria-Eleni K. & Aslanidis, Nektarios & Kouretas, Georgios P., 2022. "U.S. banks’ lending, financial stability, and text-based sentiment analysis," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 73-90.
    20. Laborda, Ricardo & Olmo, Jose, 2017. "Optimal asset allocation for strategic investors," International Journal of Forecasting, Elsevier, vol. 33(4), pages 970-987.
    21. Li, Yulin, 2021. "Investor sentiment and sovereign bonds," Journal of International Money and Finance, Elsevier, vol. 115(C).
    22. Laborda, Ricardo & Muñoz, Fernando, 2016. "Optimal allocation of government bond funds through the business cycle. Is money smart?," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 46-67.
    23. Oğuzhan Çepni & Rangan Gupta & Mark E. Wohar, 2021. "Variants of consumption‐wealth ratios and predictability of U.S. government bond risk premia," International Review of Finance, International Review of Finance Ltd., vol. 21(2), pages 661-674, June.
    24. Juan Andrés Espinosa Torres & Luis Fernando Melo Velandia & José Fernando Moreno Gutiérrez, 2014. "Estimación de la prima por vencimiento de los TES en pesos del gobierno colombiano," Borradores de Economia 12333, Banco de la Republica.
    25. Bansal, Naresh & Connolly, Robert A. & Stivers, Chris, 2015. "Equity volatility as a determinant of future term-structure volatility," Journal of Financial Markets, Elsevier, vol. 25(C), pages 33-51.
    26. Oguzhan Cepni & Rangan Gupta & I. Ethem Guney & M. Hasan Yilmaz, 2019. "Forecasting Local Currency Bond Risk Premia of Emerging Markets: The Role of Cross-Country Macro-Financial Linkages," Working Papers 201957, University of Pretoria, Department of Economics.
    27. Bouri, Elie & Demirer, Riza & Gupta, Rangan & Wohar, Mark E., 2021. "Gold, platinum and the predictability of bond risk premia," Finance Research Letters, Elsevier, vol. 38(C).
    28. Louis RAFFESTIN, 2016. "Do bond credit ratings lead to excess comovement," LEO Working Papers / DR LEO 2481, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    29. Roland Fuess & Massimo Guidolin & Christian Koeppel, 2019. "Sentiment Risk Premia in the Cross-Section of Global Equity and Currency Returns," BAFFI CAREFIN Working Papers 19116, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    30. Çepni, Oğuzhan & Guney, I. Ethem & Gupta, Rangan & Wohar, Mark E., 2020. "The role of an aligned investor sentiment index in predicting bond risk premia of the U.S," Journal of Financial Markets, Elsevier, vol. 51(C).
    31. Islam, Mohd. Anisul, 2021. "Investor sentiment in the equity market and investments in corporate-bond funds," International Review of Financial Analysis, Elsevier, vol. 78(C).

  27. Jesus Gonzalo & Jose Olmo, 2014. "Conditional Stochastic Dominance Tests In Dynamic Settings," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 55(3), pages 819-838, August.
    See citations under working paper version above.
  28. Mark Hallam & Jose Olmo, 2014. "Semiparametric Density Forecasts of Daily Financial Returns from Intraday Data," Journal of Financial Econometrics, Oxford University Press, vol. 12(2), pages 408-432.

    Cited by:

    1. Jonas Dovern & Hans Manner, 2018. "Order Invariant Tests for Proper Calibration of Multivariate Density Forecasts," CESifo Working Paper Series 7023, CESifo.
    2. Dovern, Jonas & Manner, Hans, 2016. "Robust Evaluation of Multivariate Density Forecasts," VfS Annual Conference 2016 (Augsburg): Demographic Change 145547, Verein für Socialpolitik / German Economic Association.
    3. Halbleib, Roxana & Dimitriadis, Timo, 2019. "How informative is high-frequency data for tail risk estimation and forecasting? An intrinsic time perspectice," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203669, Verein für Socialpolitik / German Economic Association.
    4. Gao, Bin & Yang, Chunpeng, 2017. "Forecasting stock index futures returns with mixed-frequency sentiment," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 69-83.
    5. Dovern, Jonas & Manner, Hans, 2016. "Order Invariant Evaluation of Multivariate Density Forecasts," Working Papers 0608, University of Heidelberg, Department of Economics.
    6. Song, Shijia & Li, Handong, 2023. "A method for predicting VaR by aggregating generalized distributions driven by the dynamic conditional score," The Quarterly Review of Economics and Finance, Elsevier, vol. 88(C), pages 203-214.
    7. Hallam, Mark & Olmo, Jose, 2014. "Forecasting daily return densities from intraday data: A multifractal approach," International Journal of Forecasting, Elsevier, vol. 30(4), pages 863-881.

  29. Hallam, Mark & Olmo, Jose, 2014. "Forecasting daily return densities from intraday data: A multifractal approach," International Journal of Forecasting, Elsevier, vol. 30(4), pages 863-881.

    Cited by:

    1. Halbleib, Roxana & Dimitriadis, Timo, 2019. "How informative is high-frequency data for tail risk estimation and forecasting? An intrinsic time perspectice," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203669, Verein für Socialpolitik / German Economic Association.
    2. Gao, Bin & Yang, Chunpeng, 2017. "Forecasting stock index futures returns with mixed-frequency sentiment," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 69-83.
    3. Méndez-Gordillo, Alma Rosa & Campos-Amezcua, Rafael & Cadenas, Erasmo, 2022. "Wind speed forecasting using a hybrid model considering the turbulence of the airflow," Renewable Energy, Elsevier, vol. 196(C), pages 422-431.
    4. Markus Vogl, 2022. "Quantitative modelling frontiers: a literature review on the evolution in financial and risk modelling after the financial crisis (2008–2019)," SN Business & Economics, Springer, vol. 2(12), pages 1-69, December.

  30. Antonio Galvao & Kengo Kato & Gabriel Montes-Rojas & Jose Olmo, 2014. "Testing linearity against threshold effects: uniform inference in quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(2), pages 413-439, April.

    Cited by:

    1. Chung-Ming Kuan & Christos Michalopoulos & Zhijie Xiao, 2017. "Quantile Regression on Quantile Ranges – A Threshold Approach," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(1), pages 99-119, January.
    2. Liwen Zhang & Huixia Judy Wang & Zhongyi Zhu, 2017. "Composite change point estimation for bent line quantile regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(1), pages 145-168, February.
    3. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
    4. Heejoon Han & Oliver Linton & Tatsushi Oka & Yoon-Jae Whang, 2014. "The Cross-Quantilogram: Measuring Quantile Dependence and Testing Directional Predictability between Time Series," Cambridge Working Papers in Economics 1452, Faculty of Economics, University of Cambridge.
    5. Christoph Rothe & Dominik Wied, 2013. "Misspecification Testing in a Class of Conditional Distributional Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 314-324, March.
    6. Junho Lee & Ying Sun & Huixia Judy Wang, 2021. "Spatial cluster detection with threshold quantile regression," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
    7. Christis Katsouris, 2023. "Estimation and Inference in Threshold Predictive Regression Models with Locally Explosive Regressors," Papers 2305.00860, arXiv.org, revised May 2023.
    8. Sokbae (Simon) Lee & Hyunmin Park & Myung Hwan Seo & Youngki Shin, 2014. "A contribution to the Reinhart and Rogoff debate: not 90 percent but maybe 30 percent," CeMMAP working papers CWP39/14, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    9. Martins, Luis F., 2021. "The US debt–growth nexus along the business cycle," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).

  31. Laborda, Juan & Laborda, Ricardo & Olmo, Jose, 2014. "Optimal currency carry trade strategies," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 52-66.

    Cited by:

    1. Yamani, Ehab, 2019. "Diversification role of currency momentum for carry trade: Evidence from financial crises," Journal of Multinational Financial Management, Elsevier, vol. 49(C), pages 1-19.
    2. Lei Pan & Svetlana Maslyuk-Escobedo & Vinod Mishra, 2019. "Carry Trade Returns and Commodity Prices under Capital and Interest Rate Controls: Empirical Evidence from China," Monash Economics Working Papers 16-18, Monash University, Department of Economics.
    3. Chang‐Che Wu & MeiChi Huang & Chih‐Chiang Wu, 2021. "The role of asymmetry and dynamics in carry trade and general financial markets," The Financial Review, Eastern Finance Association, vol. 56(2), pages 331-353, May.
    4. Lumengo Bonga-Bonga & Tebogo Maake, 2021. "The Relationship between Carry Trade and Asset Markets in South Africa," JRFM, MDPI, vol. 14(7), pages 1-13, July.
    5. Laborda, Ricardo, 2018. "Optimal combination of currency strategies," The North American Journal of Economics and Finance, Elsevier, vol. 43(C), pages 129-140.
    6. Dupuy, Philippe, 2021. "Risk-adjusted return managed carry trade," Journal of Banking & Finance, Elsevier, vol. 129(C).

  32. Yuzhi Cai & Gabriel Montes‐Rojas & Jose Olmo, 2013. "Quantile Double AR Time Series Models for Financial Returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(6), pages 551-560, September.

    Cited by:

    1. Zhu, Huafeng & Zhang, Xingfa & Liang, Xin & Li, Yuan, 2017. "On a vector double autoregressive model," Statistics & Probability Letters, Elsevier, vol. 129(C), pages 86-95.
    2. Zhu Huafeng & Zhang Xingfa & Liang Xin & Li Yuan, 2018. "Moving Average Model with an Alternative GARCH-Type Error," Journal of Systems Science and Information, De Gruyter, vol. 6(2), pages 165-177, April.
    3. Kai Yang & Qingqing Zhang & Xinyang Yu & Xiaogang Dong, 2023. "Bayesian inference for a mixture double autoregressive model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 77(2), pages 188-207, May.
    4. Gareth W. Peters, 2018. "General Quantile Time Series Regressions for Applications in Population Demographics," Risks, MDPI, vol. 6(3), pages 1-47, September.

  33. Fuertes, Ana-Maria & Olmo, Jose, 2013. "Optimally harnessing inter-day and intra-day information for daily value-at-risk prediction," International Journal of Forecasting, Elsevier, vol. 29(1), pages 28-42.

    Cited by:

    1. Meng, Xiaochun & Taylor, James W., 2018. "An approximate long-memory range-based approach for value at risk estimation," International Journal of Forecasting, Elsevier, vol. 34(3), pages 377-388.
    2. Jayawardena, Nirodha I. & Todorova, Neda & Li, Bin & Su, Jen-Je, 2020. "Volatility forecasting using related markets’ information for the Tokyo stock exchange," Economic Modelling, Elsevier, vol. 90(C), pages 143-158.
    3. Marius Matei & Xari Rovira & Núria Agell, 2019. "Bivariate Volatility Modeling with High-Frequency Data," Econometrics, MDPI, vol. 7(3), pages 1-15, September.
    4. Lazar, Emese & Xue, Xiaohan, 2020. "Forecasting risk measures using intraday data in a generalized autoregressive score framework," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1057-1072.
    5. Fei, Fei & Fuertes, Ana-Maria & Kalotychou, Elena, 2017. "Dependence in credit default swap and equity markets: Dynamic copula with Markov-switching," International Journal of Forecasting, Elsevier, vol. 33(3), pages 662-678.
    6. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2014. "Realized volatility models and alternative Value-at-Risk prediction strategies," Economic Modelling, Elsevier, vol. 40(C), pages 101-116.
    7. Santos, Douglas G. & Candido, Osvaldo & Tófoli, Paula V., 2022. "Forecasting risk measures using intraday and overnight information," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    8. Andrada-Félix, Julián & Fernández-Rodríguez, Fernando & Fuertes, Ana-Maria, 2016. "Combining nearest neighbor predictions and model-based predictions of realized variance: Does it pay?," International Journal of Forecasting, Elsevier, vol. 32(3), pages 695-715.
    9. Reber, Beat, 2017. "Does mispricing, liquidity or third-party certification contribute to IPO downside risk?," International Review of Financial Analysis, Elsevier, vol. 51(C), pages 25-53.
    10. Lu-Tao Zhao & Li-Na Liu & Zi-Jie Wang & Ling-Yun He, 2019. "Forecasting Oil Price Volatility in the Era of Big Data: A Text Mining for VaR Approach," Sustainability, MDPI, vol. 11(14), pages 1-20, July.
    11. Seyed Mohammad Sina Seyfi & Azin Sharifi & Hamidreza Arian, 2020. "Portfolio Risk Measurement Using a Mixture Simulation Approach," Papers 2011.07994, arXiv.org.
    12. Ana-Maria Fuertes & Elena Kalotychou & Natasa Todorovic, 2015. "Daily volume, intraday and overnight returns for volatility prediction: profitability or accuracy?," Review of Quantitative Finance and Accounting, Springer, vol. 45(2), pages 251-278, August.
    13. Bjoern Schulte-Tillmann & Mawuli Segnon & Timo Wiedemann, 2023. "A comparison of high-frequency realized variance measures: Duration- vs. return-based approaches," CQE Working Papers 10523, Center for Quantitative Economics (CQE), University of Muenster.
    14. Bayer, Sebastian, 2018. "Combining Value-at-Risk forecasts using penalized quantile regressions," Econometrics and Statistics, Elsevier, vol. 8(C), pages 56-77.
    15. Taylor, James W., 2020. "Forecast combinations for value at risk and expected shortfall," International Journal of Forecasting, Elsevier, vol. 36(2), pages 428-441.
    16. Arian, Hamid & Moghimi, Mehrdad & Tabatabaei, Ehsan & Zamani, Shiva, 2022. "Encoded Value-at-Risk: A machine learning approach for portfolio risk measurement," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 202(C), pages 500-525.
    17. Seyfi, Seyed Mohammad Sina & Sharifi, Azin & Arian, Hamidreza, 2021. "Portfolio Value-at-Risk and expected-shortfall using an efficient simulation approach based on Gaussian Mixture Model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 1056-1079.
    18. Chiu, Yen-Chen & Chuang, I-Yuan, 2016. "The performance of the switching forecast model of value-at-risk in the Asian stock markets," Finance Research Letters, Elsevier, vol. 18(C), pages 43-51.
    19. Timo Dimitriadis & Xiaochun Liu & Julie Schnaitmann, 2020. "Encompassing Tests for Value at Risk and Expected Shortfall Multi-Step Forecasts based on Inference on the Boundary," Papers 2009.07341, arXiv.org.
    20. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    21. Ana-Maria Fuertes & Jose Olmo, 2016. "On Setting Day-Ahead Equity Trading Risk Limits: VaR Prediction at Market Close or Open?," JRFM, MDPI, vol. 9(3), pages 1-20, September.
    22. Ahoniemi, Katja & Lanne, Markku, 2013. "Overnight stock returns and realized volatility," International Journal of Forecasting, Elsevier, vol. 29(4), pages 592-604.
    23. Jan G. De Gooijer, 2023. "Penalized Averaging of Quantile Forecasts from GARCH Models with Many Exogenous Predictors," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 407-424, June.

  34. Antonio F. Galvao & Gabriel Montes-Rojas & Jose Olmo, 2013. "A panel data test for poverty traps," Applied Economics, Taylor & Francis Journals, vol. 45(14), pages 1943-1952, May.

    Cited by:

    1. Golub, A. & Potashnikov, V., 2022. "Theoretical analysis of development traps: On the example of Russia," Journal of the New Economic Association, New Economic Association, vol. 54(2), pages 56-74.

  35. Martinez Oscar & Olmo Jose, 2012. "A Nonlinear Threshold Model for the Dependence of Extremes of Stationary Sequences," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(3), pages 1-39, September.
    See citations under working paper version above.
  36. Olmo, José & Sanso-Navarro, Marcos, 2012. "Forecasting the performance of hedge fund styles," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2351-2365.

    Cited by:

    1. Panopoulou, Ekaterini & Vrontos, Spyridon, 2015. "Hedge fund return predictability; To combine forecasts or combine information?," Journal of Banking & Finance, Elsevier, vol. 56(C), pages 103-122.
    2. Laborda, Ricardo, 2018. "Optimal combination of currency strategies," The North American Journal of Economics and Finance, Elsevier, vol. 43(C), pages 129-140.

  37. J. Carlos Escanciano & Jose Olmo, 2011. "Robust Backtesting Tests for Value-at-risk Models," Journal of Financial Econometrics, Oxford University Press, vol. 9(1), pages 132-161, Winter.

    Cited by:

    1. Radu Tunaru, 2015. "Model Risk in Financial Markets:From Financial Engineering to Risk Management," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 9524, June.
    2. Farkas, Walter & Fringuellotti, Fulvia & Tunaru, Radu, 2020. "A cost-benefit analysis of capital requirements adjusted for model risk," Journal of Corporate Finance, Elsevier, vol. 65(C).
    3. Thiele, Stephen, 2019. "Detecting underestimates of risk in VaR models," Journal of Banking & Finance, Elsevier, vol. 101(C), pages 12-20.
    4. Olmo Jose & Pouliot William, 2011. "Early Detection Techniques for Market Risk Failure," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(4), pages 1-55, September.
    5. Christis Katsouris, 2023. "Quantile Time Series Regression Models Revisited," Papers 2308.06617, arXiv.org, revised Aug 2023.
    6. Christian Gouriéroux & Jean-Michel Zakoian, 2012. "Estimation Adjusted VaR," Working Papers 2012-16, Center for Research in Economics and Statistics.
    7. Igor Kheifets, 2014. "Specification Tests for Nonlinear Dynamic Models," Cowles Foundation Discussion Papers 1937, Cowles Foundation for Research in Economics, Yale University, revised Oct 2014.
    8. Francq, Christian & Zakoian, Jean-Michel, 2015. "Joint inference on market and estimation risks in dynamic portfolios," MPRA Paper 68100, University Library of Munich, Germany.
    9. Christophe Hurlin & Sébastien Laurent & Rogier Quaedvlieg & Stephan Smeekes, 2017. "Risk Measure Inference," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(4), pages 499-512, October.
    10. Sander Barendse & Erik Kole & Dick van Dijk, 2019. "Backtesting Value-at-Risk and Expected Shortfall in the Presence of Estimation Error," Tinbergen Institute Discussion Papers 19-058/III, Tinbergen Institute.
    11. Christian Francq & Jean-Michel Zakoian, 2019. "Virtual Historical Simulation for estimating the conditional VaR of large portfolios," Papers 1909.04661, arXiv.org.
    12. Filippo Curti & Marco Migueis, 2016. "Predicting Operational Loss Exposure Using Past Losses," Finance and Economics Discussion Series 2016-2, Board of Governors of the Federal Reserve System (U.S.).
    13. Escanciano, Juan Carlos & Pei, Pei, 2012. "Pitfalls in backtesting Historical Simulation VaR models," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2233-2244.
    14. Argyropoulos, Christos & Panopoulou, Ekaterini, 2019. "Backtesting VaR and ES under the magnifying glass," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 22-37.
    15. Christophe Boucher & Jon Danielsson & Patrick Kouontchou & Bertrand Maillet, 2014. "Risk models-at-risk," Post-Print hal-02312332, HAL.
    16. Zulu, Thulani & Manguzvane, Mathias Mandla & Bonga-Bonga, Lumengo, 2023. "Assessing the contribution of South African Insurance Firms to Systemic Risk," MPRA Paper 116815, University Library of Munich, Germany.
    17. Olivier de Bandt & Jean-Cyprien Héam & Claire Labonne & Santiago Tavolaro, 2015. "La mesure du risque systémique après la crise financière," Revue économique, Presses de Sciences-Po, vol. 66(3), pages 481-500.
    18. Evers, Corinna & Rohde, Johannes, 2014. "Model Risk in Backtesting Risk Measures," Hannover Economic Papers (HEP) dp-529, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    19. Jack Fosten & Daniel Gutknecht & Marc-Oliver Pohle, 2023. "Testing Quantile Forecast Optimality," Papers 2302.02747, arXiv.org, revised Oct 2023.
    20. Nieto, Maria Rosa & Ruiz, Esther, 2016. "Frontiers in VaR forecasting and backtesting," International Journal of Forecasting, Elsevier, vol. 32(2), pages 475-501.
    21. Köksal, Bülent & Orhan, Mehmet, 2012. "Market risk of developed and developing countries during the global financial crisis," MPRA Paper 37523, University Library of Munich, Germany.
    22. Claußen, Arndt & Rösch, Daniel & Schmelzle, Martin, 2019. "Hedging parameter risk," Journal of Banking & Finance, Elsevier, vol. 100(C), pages 111-121.
    23. Bogdan Wlodarczyk, 2017. "Zmiennosc cen na globalnym rynku surowcow a ryzyko banku," Problemy Zarzadzania, University of Warsaw, Faculty of Management, vol. 15(66), pages 107-124.
    24. James Ming Chen, 2018. "On Exactitude in Financial Regulation: Value-at-Risk, Expected Shortfall, and Expectiles," Risks, MDPI, vol. 6(2), pages 1-28, June.

  38. Jose Olmo & Keith Pilbeam, 2011. "Uncovered interest parity and the efficiency of the foreign exchange market: a re‐examination of the evidence," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 16(2), pages 189-204, April.

    Cited by:

    1. Beckmann, Joscha & Czudaj, Robert, 2017. "Exchange rate expectations since the financial crisis: Performance evaluation and the role of monetary policy and safe haven," Journal of International Money and Finance, Elsevier, vol. 74(C), pages 283-300.
    2. Elias, Nikolaos & Smyrnakis, Dimitris & Tzavalis, Elias, 2022. "Predicting future exchange rate changes based on interest rates and holding-period returns differentials net of the forward risk premium effects," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 694-715.
    3. Michael D. Goldberg & Olesia Kozlova & Deniz Ozabaci, 2020. "Forward Rate Bias in Developed and Developing Countries: More Risky Not Less Rational," Econometrics, MDPI, vol. 8(4), pages 1-26, December.
    4. Ladislav Kristoufek & Miloslav Vosvrda, 2015. "Gold, currencies and market efficiency," Papers 1510.08615, arXiv.org.
    5. Daniel L. Thornton, 2007. "Resolving the unbiasedness and forward premium puzzles," Working Papers 2007-014, Federal Reserve Bank of St. Louis.
    6. Katarzyna Czech & Łukasz Pietrych, 2021. "The Efficiency of the Polish Zloty Exchange Rate Market: The Uncovered Interest Parity and Fractal Analysis Approaches," Risks, MDPI, vol. 9(8), pages 1-17, August.
    7. Yutaka Kurihara, 2015. "Are Japanese Stock Prices Important Deterministic Elements of Exchange Rate Returns?," Bulletin of Applied Economics, Risk Market Journals, vol. 2(2), pages 1-9.
    8. Katarzyna Anna Czech, & Adam Waszkowski, 2012. "Foreign Exchange Market Efficiency. Empirical Results For The Usd/Eur Market," "e-Finanse", University of Information Technology and Management, Institute of Financial Research and Analysis, vol. 8(3), pages 1-9, October.
    9. Kohlscheen, Emanuel, 2014. "The impact of monetary policy on the exchange rate: A high frequency exchange rate puzzle in emerging economies," Journal of International Money and Finance, Elsevier, vol. 44(C), pages 69-96.
    10. H. Kent Baker & Satish Kumar & Kirti Goyal & Prashant Gupta, 2023. "International journal of finance and economics: A bibliometric overview," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 9-46, January.
    11. Lucjan Orlowski & Carolyne Soper & Monika Sywak, 2023. "Uncovered equity returns parity in non‐euro Central European EU member countries," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 307-315, January.
    12. Jorge Andrés Muñoz Mendoza & Carmen Lissette Veloso Ramos & Sandra María Sepúlveda Yelpo & Carlos Leandro Delgado Fuentealba & Edinson Edgardo Cornejo Saavedra, 2022. "Exchange Markets and Stock Markets Integration in Latin-America," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 17(3), pages 1-24, Julio - S.
    13. Efthymios Argyropoulos & Nikolaos Elias & Dimitris Smyrnakis & Elias Tzavalis, 2021. "Can country-specific interest rate factors explain the forward premium anomaly?," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 45(2), pages 252-269, April.

  39. Keith Pilbeam & Jose Olmo, 2011. "The forward discount puzzle and market efficiency," Annals of Finance, Springer, vol. 7(1), pages 119-135, February.

    Cited by:

    1. Azzam, Islam & El-Masry, Ahmed A. & Yamani, Ehab, 2023. "Foreign exchange market efficiency during COVID-19 pandemic," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 717-730.
    2. Yutaka Kurihara, 2015. "Are Japanese Stock Prices Important Deterministic Elements of Exchange Rate Returns?," Bulletin of Applied Economics, Risk Market Journals, vol. 2(2), pages 1-9.
    3. Beckmann, Joscha & Belke, Ansgar & Czudaj, Robert, 2014. "Regime-dependent adjustment in energy spot and futures markets," Economic Modelling, Elsevier, vol. 40(C), pages 400-409.
    4. Ahmad, Rubi & Rhee, S. Ghon & Wong, Yuen Meng, 2012. "Foreign exchange market efficiency under recent crises: Asia-Pacific focus," Journal of International Money and Finance, Elsevier, vol. 31(6), pages 1574-1592.
    5. Norman C. Miller, 2014. "Exchange Rate Economics," Books, Edward Elgar Publishing, number 14981, December.

  40. Olmo, Jose & Pilbeam, Keith & Pouliot, William, 2011. "Detecting the presence of insider trading via structural break tests," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2820-2828, November.

    Cited by:

    1. Michael Buchner & Tobias A. Jopp, 2019. "Full steam ahead: Insider knowledge, stock trading and the nationalization of the railways in Prussia around 1879," Working Papers 0151, European Historical Economics Society (EHES).
    2. Hanedar, Avni Önder & Yaldız Hanedar, Elmas & Göktan, Mehmet Gökhan, 2022. "Insider trading on Ottoman sovereign default: The Ottoman General Debt Bond at European and İstanbul financial markets," Finance Research Letters, Elsevier, vol. 47(PB).
    3. Nguyen, Vinh & Tran, Anh & Zeckhauser, Richard, 2017. "Stock splits to profit insider trading: Lessons from an emerging market," Journal of International Money and Finance, Elsevier, vol. 74(C), pages 69-87.
    4. Keshab Bhattarai, 2015. "Financial deepening and economic growth," Applied Economics, Taylor & Francis Journals, vol. 47(11), pages 1133-1150, March.
    5. Jonathan A. Batten & Igor Lončarski & Peter G. Szilagyi, 2018. "When Kamay Met Hill: Organisational Ethics in Practice," Journal of Business Ethics, Springer, vol. 147(4), pages 779-792, February.
    6. Sahbi FARHANI, 2012. "Tests of Parameters Instability: Theoretical Study and Empirical Analysis on Two Types of Models (ARMA Model and Market Model)," International Journal of Economics and Financial Issues, Econjournals, vol. 2(3), pages 246-266.
    7. Simon de Bonviller & Alec Zuo & Sarah Ann Wheeler, 2019. "Is there evidence of insider trading in Australian water markets?," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 63(2), pages 307-327, April.
    8. J. James Reade & Sachiko Akie, 2013. "Using Forecasting to Detect Corruption in International Football," Working Papers 2013-005, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    9. James Reade, 2014. "Detecting corruption in football," Chapters, in: John Goddard & Peter Sloane (ed.), Handbook on the Economics of Professional Football, chapter 25, pages 419-446, Edward Elgar Publishing.
    10. Keshab Bhattarai, 2015. "Financial Deepening and Economic Growth in Advanced and Emerging Economies," Review of Development Economics, Wiley Blackwell, vol. 19(1), pages 178-195, February.
    11. Pouliot, William, 2016. "Robust tests for change in intercept and slope in linear regression models with application to manager performance in the mutual fund industry," Economic Modelling, Elsevier, vol. 58(C), pages 523-534.
    12. Xihan Xiong & Zhipeng Wang & Tianxiang Cui & William Knottenbelt & Michael Huth, 2023. "Market Misconduct in Decentralized Finance (DeFi): Analysis, Regulatory Challenges and Policy Implications," Papers 2311.17715, arXiv.org, revised Mar 2024.
    13. James, Robert & Leung, Henry & Prokhorov, Artem, 2023. "A machine learning attack on illegal trading," Journal of Banking & Finance, Elsevier, vol. 148(C).
    14. John Goddard & Peter Sloane (ed.), 2014. "Handbook on the Economics of Professional Football," Books, Edward Elgar Publishing, number 14821, December.
    15. Batten, Jonathan A. & Lončarski, Igor & Szilagyi, Peter G., 2021. "Strategic insider trading in foreign exchange markets," Journal of Corporate Finance, Elsevier, vol. 69(C).
    16. Jose Olmo & William Pouliot, 2014. "Tests to Disentangle Breaks in Intercept from Slope in Linear Regression Models with Application to Management Performance in the Mutual Fund Industry," Discussion Papers 14-02, Department of Economics, University of Birmingham.
    17. Cline, Brandon N. & Posylnaya, Valeriya V., 2019. "Illegal insider trading: Commission and SEC detection," Journal of Corporate Finance, Elsevier, vol. 58(C), pages 247-269.
    18. Luke M. Bennett & Wei Hu, 2023. "Filtration enlargement‐based time series forecast in view of insider trading," Journal of Economic Surveys, Wiley Blackwell, vol. 37(1), pages 112-140, February.

  41. Olmo Jose & Pouliot William, 2011. "Early Detection Techniques for Market Risk Failure," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(4), pages 1-55, September.
    See citations under working paper version above.
  42. Antonio F. Galvao Jr. & Gabriel Montes‐Rojas & Jose Olmo, 2011. "Threshold quantile autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(3), pages 253-267, May.
    See citations under working paper version above.
  43. Escanciano, J. Carlos & Olmo, Jose, 2010. "Backtesting Parametric Value-at-Risk With Estimation Risk," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 36-51.
    See citations under working paper version above.
  44. Olmo, Jose & Pilbeam, Keith, 2009. "Uncovered Interest Parity: Are Empirical Rejections of It Valid?," Journal of Economic Integration, Center for Economic Integration, Sejong University, vol. 24, pages 369-384.

    Cited by:

    1. Bhatti, Razzaque H., 2014. "The existence of uncovered interest parity in the CIS countries," Economic Modelling, Elsevier, vol. 40(C), pages 227-241.

  45. Jose Olmo & Keith Pilbeam, 2009. "The profitability of carry trades," Annals of Finance, Springer, vol. 5(2), pages 231-241, March.

    Cited by:

    1. Keith Pilbeam & Jose Olmo, 2011. "The forward discount puzzle and market efficiency," Annals of Finance, Springer, vol. 7(1), pages 119-135, February.
    2. Vistesen, Claus, 2008. "Of Low Yielders and Carry Trading – the JPY and CHF as Market Risk Sentiment Gauges," MPRA Paper 9952, University Library of Munich, Germany.
    3. Claus VISTESEN, 2009. "Carry Trade Fundamentals And The Financial Crisis 2007-2010," Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 4(2(8)_ Sum).

  46. Jose Olmo, 2008. "On the role of volatility for modelling risk exposure," International Journal of Monetary Economics and Finance, Inderscience Enterprises Ltd, vol. 1(2), pages 219-234.

    Cited by:

    1. Lambert, Philippe & Laurent, Sébastien & Veredas, David, 2012. "Testing conditional asymmetry: A residual-based approach," Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1229-1247.

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